### 17. notes that the commission’s vademecum on public procurement was last updated in january 2020; notes that the current definition of ‘professional conflict of interest’ is limited to a conflicting interest that affects the capacity of an economic operator to perform a contract; calls on the commission to provide for a more explicit definition and to ensure that its implementation rules on public procurement do not permit the awarding of policy-related service contracts to undertakings that are under the economic control of a parent company or a group that owns shares related to activities that are not in line with the eu’s environmental , social and green deal objectives; ### 17. notes that the commission’s vademecum on public procurement was last updated in january 2020 but has not been made public nor shared with the european parliament for opinion; requests that the parliament should be able to formulate an opinion on future revision of the commission’s vademecum on public procurement; calls on the commission to share regular updates with the parliament’s relevant committees on the revision of its vademecum before it is finalised either through the discharge procedure of the european commission or during dedicated hearings;


### 23. encourages the commission to exploit the opportunities for simplified cost options to their full potential; considers that the focus of checks should be on ex ante verification of the calculations of the simplified cost options , while ex post verifications should be used to improve the system of calculation , except in cases of suspected fraud; ### 23. encourages the commission and member states to exploit the opportunities for simplified cost options to their full potential; considers that the focus of checks should be on ex ante verification of the calculations of the simplified cost options , while ex post verifications should be used to improve the system of calculation , except in cases of suspected fraud;


### 1. notes that the world stands on the verge of the fourth industrial revolution; points out that in comparison with the three previous waves , initiated by the introduction of steam , electricity , and then computers , the fourth wave draws its energy from an abundance of data combined with powerful algorithms; stresses that today’s digital revolution is shaped by its unprecedented scale , fast convergence , and the enormous impact of emerging technological breakthroughs on states , economies and societies; ### 1. notes that the onset of digital technologies , created by an abundance of data combined with powerful algorithms and unprecedented computing power , is changing economies , societies and international relations at a breath-taking pace; notes that when used to serve the common good , artificial intelligence can have enormous benefits; emphasises that the digital transition must be shaped in full respect of fundamental rights and in such a way that digital technologies serve humanity;


### 2. observes that the digital revolution has , at the same time , triggered a global tech race , in which digital sovereignty is seen as a prerequisite for great-power status in both political and economic terms; stresses the growing realisation among decision makers that emerging technologies could lead to a global power shift away from the western world; ### 2. recognises the geopolitical dimension of the development of ai , which results from the tremendous economic value and technological capabilities that accumulate in those economies that commit the most resources to the research , development and marketing of ai applications; takes note that digital sovereignty has become a policy objective in many countries


### 3. points out that europe , which for centuries set international standards , dominated technological progress and led in high-end manufacturing and deployment , has therefore fallen behind in a new ‘winner-takes-most’ or ‘superstar’ economy; underlines the risk of european values being globally replaced , our companies becoming marginalised and our living standards being drastically reduced; ### 3. points out that europe , which for centuries set international standards , dominated technological progress and led in high-end manufacturing and deployment , invests far less in ai technologies than the united states or china , while remaining competitive in ai thematic research output19a; points out that only eight of today’s top 200 digital companies are domiciled in the eu;


### 4. highlights , firstly , that digital tools are increasingly becoming an instrument of manipulation in the hands of authoritarian states and their proxies , used to trigger a clash between political systems; explains that digital espionage , low-scale warfare and disinformation campaigns are deployed in order to pose an existential threat to our democratic societies and question the european way of life; ### 4. emphasises that the enormous potential of ai also carries substantial risks to abuse by public authorities , especially in autocratic or totalitarian systems; highlights that digital tools are increasingly becoming an instrument of manipulation in the hands of authoritarian states and their proxies; explains that digital espionage , low-scale warfare and disinformation campaigns pose a threat to our democratic societies and question the european way of life , in which individuals must always have a choice to disconnect themselves from ubiquitous data collection in their daily lives;


### 5. underlines , secondly , that the eu is failing to commercialise its ground-breaking technological innovations , thereby enabling fast-growing non-european corporations to take our best ideas , talent and companies; points out that , as a result , only eight of today’s top 200 digital companies are domiciled in the eu , while our economic growth is constantly declining; notes that europe’s high wages and the world’s most generous social welfare systems are financially dependent on us competing with the rest of the world; ### 5. stresses that the nature of digital business models allows for great degrees of scalability and network effects; points out that many digital markets are characterised by a high degree of market concentration , allowing a small number of currently mostly us-based tech platforms to lead the commercialisation of ground-breaking technological innovations , attract the best ideas and talent , and reach extraordinary profitability;


### 6. warns that as a result of these and other existential threats to our democracy and prosperity , the global tech race has become a fight for survival for the eu; stresses that if the eu does not act swiftly and courageously , it will end up becoming a digital colony of china , the us and other states and risk losing its political stability , social security and individual liberties; ### deleted


### 7. argues that artificial intelligence (ai) is the key emerging technology within the fourth industrial revolution; notes that ai is the control centre of the new data layer that surrounds us and which can be thought of as the fifth element after air , earth , water and fire; states that by 2030 , ai is expected to contribute more than eur 11 billion to the global economy , an amount that almost matches china’s gdp in 2020; ### 7. argues that artificial intelligence (ai) is a key emerging technology within the fourth industrial revolution , which allows for the introduction of innovative products and services , has the power to increase consumer choice and convenience or to render production processes more efficient; stresses that , at the same time , ai technologies have the risk to reduce human agency and to limit the ability of individuals to self-determine the degree of their interaction with digital technology in daily life; recognizes that radical change of this scale impacts various parts of society differently , depending on their objectives , geographic location , or socio-economic context;


### 8. explains that there is therefore a race for ai leadership within the global tech race; points out that the countries that master ai will gain key advantages; highlights , however , that ai is not a technology with magical powers but rather an efficient tool that we can put to good use; states that the rise of ai likewise does not require us to completely rewrite our laws to counter new kind of threats or to prevent intelligent machines from taking over; believes that although ai is indeed reshaping the world as we know it , the reality is much less dramatic and most developments in the field of ai are positive; ### 8. highlights that the introduction of ai technologies promises to deliver immense economic value to those who profitably develop , produce or , successfully adopt such technologies , as well as to those economies and countries in which such value creation takes place; stresses , however , that ai is not a technology with magical powers but rather a tool that can be put to good use; states that the rise of ai likewise does not require us to completely rewrite our laws; believes that ai is reshaping the world as we know it;


### 9. explains that ai is actually an umbrella term that covers a wide range of old and new technologies that often have little more in common than being guided by a given set of human-defined objectives and having some degree of autonomy in their actions; notes that while some of these technologies are already in widespread use , others are still under development or are even just speculative concepts that may or may not exist in the future; ### 9. recalls that ai is based on software using probabilistic models and algorithmic prediction for a set of specific objectives; stresses that ai is an umbrella term , covering a wide range of old and new technologies that often have little more in common than being guided by a given set of objectives and having some degree of autonomy in their actions; notes that while some of these technologies are already in widespread use , others are still under development or are even just speculative concepts that may or may not exist in the future;


### 11. notes that in the second wave , the automated learning processes of algorithms based on the processing of large amounts of high-quality data , the ability to bring together inputs from multiple radars , lidars and cameras to form a single image of the environment , and the identification of patterns made ai systems more complex , autonomous and opaque; stresses that current ai can therefore be broken down into many different sub-domains and techniques , whereby deep learning is for instance a subfield of machine learning , which itself is a subfield of ai; ### 11. notes that in the second wave , the automated learning processes of algorithms based on the processing of large amounts of data , the ability to bring together inputs from multiple radars , lidars and cameras to form a single image of the environment , and the identification of patterns made ai systems more complex , autonomous and opaque; stresses that current ai can therefore be broken down into many different sub-domains and techniques , whereby deep learning is for instance a subfield of machine learning , which itself is a subfield of ai;


### 12. notes that although today’s ai has become much more powerful than symbolic ai , it can still only solve tasks in domain-specific niches such as chess or facial recognition and does not understand the actions it performs; points out that it is therefore referred to as ‘narrow’ or ‘weak’ ai and is still not more than a tool that provides recommendations and predictions; explains that self-driving cars operate , for instance , through a combination of various one-task ai systems that together are able to provide a three-dimensional map of the surroundings of the vehicle so that its operating system can make the appropriate decisions; ### 12. notes that although today’s ai has become much more powerful than symbolic ai , it can still only solve tasks in domain-specific niches such as chess or facial recognition and does not understand the actions it performs; points out that it is therefore referred to as ‘narrow’ or ‘weak’ ai and is still not more than a tool that provides recommendations and predictions; notes that self-driving cars operate , for instance , through a combination of various one-task ai systems that together are able to provide a three-dimensional map of the surroundings of the vehicle so that its operating system can make decisions;


### 13. highlights that many fears linked to ai are based on hypothetical concepts such as general ai , artificial superintelligence and singularity which could , in theory , lead to a power shift from humans to ai and create machines that could even break free from human control; stresses , however , that there are significant doubts as to whether this speculative ai can even be achieved with our technologies and scientific laws; ### 13. highlights that , while numerous harms , such as racial and sexual discrimination are demonstrably already attributable to ai , many fears linked to ai are based on hypothetical concepts such as general ai , artificial superintelligence and singularity which could , in theory , lead to a power shift from humans to ai and create machines that could even break free from human control; stresses , however , that there are significant doubts as to whether this speculative ai can even be achieved with our technologies and scientific laws;


### 14. underlines that , on the contrary , the vast majority of ai systems currently in use , are almost or even completely risk-free; refers , for instance , to automatic translations , ‘eureka machines’ , gaming machines and robots that execute repetitive manufacturing processes; concludes that only a very small number of use cases can be categorised as risky and that only such cases require regulatory action and effective safeguards; ### 14. underlines that , on the contrary , many ai systems currently in use , are almost or even completely risk-free; refers , for instance , to ‘eureka machines’ , and robots that execute repetitive manufacturing processes; whereas even apparently risk-free ai applications , such as automatic translations can repeat and reinforce linguistic or societal discrimination;


### 15. considers that the public debate should therefore be more focused on the enormous potential of ai; believes that ai offers humankind the unique chance to improve almost every area of our lives , from combating global societal challenges such as climate change , pandemics and starvation , to enhancing quality of life through personalised medicine , fitness programmes and assisted living; ### deleted


### 16. explains that the present report addresses six ai case studies in detail , outlining the opportunities offered by ai in the respective sector , the risks to be addressed and the obstacles currently preventing us from fully harnessing the benefits of ai; highlights that the case studies represent some of the most important ai use cases and , at the same time , reflect the main topics of the public hearings held by the aida committee during its mandate , namely health , the green deal , external policy and security , competitiveness , the future of democracy and the labour market; ### 16. finds that the present report addresses six ai case studies in detail , outlining the opportunities offered by ai in the respective sector , the risks to be addressed and the obstacles currently preventing us from fully harnessing the benefits of ai; highlights that the case studies represent some of the most important ai use cases and , at the same time , reflect the main topics of the public hearings held by the aida committee during its mandate , namely health , the green deal , external policy and security , competitiveness , the future of democracy and the labour market;


### 17. stresses that ai can unlock solutions in the health sector that could save millions of lives , improve our standards of living and bring a competitive edge to the european ict sector; ### 17. finds that the methodological analysis of large amounts of data , including with ai , can unlock new solutions or improve existing techniques in diagnostics and treatment and facilitate the development and improvement of patient care in the health sector;


### 18. underlines that ai is already being used to detect diseases and abnormalities at an early stage and more accurately through real-time pattern recognition and image processing , thus speeding up diagnosis and treatment and reducing unnecessary biopsies; ### 18. underlines that ai is already being used to detect diseases and abnormalities at an early stage and more accurately through real-time pattern recognition and image processing , thus speeding up diagnosis and treatment and reducing unnecessary interventions;


### 19. highlights that ai has the potential to speed up the development of new drugs , treatments and vaccines at a lower cost , while improving the quality and overall safety of the production process; finds that ai can help predict the outcome of and responses to treatments with increasing levels of accuracy when based on high-quality data , thus increasing the effectiveness of preventive care; ### 19. highlights that ai technologies can be applied to research , development and mass production of pharmaceuticals and have the potential to speed up the development of new drugs , treatments and vaccines at a lower cost to pharmaceutical companies; finds that ai can help predict the responses to treatments with increasing levels of accuracy when based on high-quality data;


### 20. underlines that ai has the potential to tailor treatments and drug development to specific patient needs and enhance engagement with stakeholders and participants in the healthcare system; finds that ai and access to datasets increase the potential for healthcare professionals to better understand the patterns and symptoms of their patients and therefore provide better feedback , guidance and support; ### 20. finds that ai and access to datasets increase the potential for healthcare professionals to better understand patterns and symptoms in their patients;


### 21. finds that the fight against covid-19 has both accelerated research into and the deployment of new technologies , notably ai applications , in the quest for improved case detection , and heightened the need for industry and publicly funded research to be able to deploy ai to strengthen the monitoring and modelling of the spread of future pandemics , without excessive limitations on freedom of movement , the infringement of data protection principles or the risk of establishing excessive surveillance regimes; ### 21. finds that the fight against covid-19 has both accelerated research into and the deployment of new technologies , notably ai applications , in the quest for improved case detection , and heightened the importance of funding and high quality data for the purpose of monitoring and modelling during future pandemics , without the infringement of data protection laws and without establishing excessive surveillance regimes;


### 22. highlights the potential of ai systems to relieve healthcare systems , and especially medical staff , by supporting routine tasks such as patient transport and reminding patients of their medication , and to remedy challenges posed by rapidly ageing populations; ### 22. highlights the potential of ai systems to relieve healthcare systems , and especially medical staff , by supporting repetitive tasks , thereby contributing to solutions to provide care to rapidly ageing populations in europe and the world;


### 23. stresses that consumer health applications based on ai can help to track an individual’s health status , yield data which can apply to early triage questions and encourage healthy behaviour , thus reducing the need to seek advice from a healthcare professional; ### 23. stresses that commercial health applications based on ai can help to track an individual’s health status and encourage healthy behaviour; points out , however , the particular sensitivity of personal health data and to the risk of data breaches or misuses also in this area;


### 24. stresses that ai in the health sector is particularly dependent on large amounts of personal data , data sharing , data accessibility and data interoperability to realise the full potential of ai and health , which are currently lacking; stresses the need to combat mistrust and to educate and better inform citizens about the benefits of ai in the field of health; ### 24. stresses that ai in the health sector is particularly dependent on large amounts of data , data sharing , data accessibility and data interoperability; stresses the need to combat mistrust with effective protection of personal data and strong cybersecurity and to educate and better inform citizens about the benefits of ai in the field of health;


### 25. stresses that additional legal steps , time and purpose limitations introduced by the gdpr , and differing interpretations across member states have led to legal uncertainty and a lack of cooperation in the health sector; underlines that specific consent obligations hinder the processing of used medical data for further analysis and studies; stresses that this leads to lengthy delays to scientific discoveries and a significant bureaucratic burden in health research20 ; ### 25. stresses that additional safeguards introduced by the gdpr , together with the research exceptions , ensure an environment which both protects and promotes trust , while nurturing research;


### 26. underlines that automatic decision-making in healthcare applications may pose risks to patients’ well-being , although ai already outperforms doctors’ diagnoses in several instances , such as breast cancer21 ; finds that current liability frameworks do not provide sufficient legal certainty over who is accountable in the event of misdiagnosis through ai , for example; ### 26. underlines that automatic decision-making in healthcare applications may pose risks to individuals’ well-being and fundamental rights , including through the misallocation of resources to individuals based on faulty or biased categorisations or prioritisations , malfunctioning technology leading to misdiagnosis , maltreatment or a choice not to treat at all; finds that current liability frameworks do not provide sufficient legal certainty over who is accountable in such cases;


### 104. calls for the creation of an adequately resourced mechanism to supervise the uniform , eu-wide implementation and enforcement of the upcoming ai laws; prefers a european ai board over the creation of a costly new eu agency for ai; suggests , however , that this board should be made up of not only the national ai supervisory authorities and the european data protection board (edpb) , but also a broad range of relevant eu bodies , such as the eu agency for fundamental rights , the high-level expert group on ai , the eu agency for cybersecurity , the european consumer consultative group , and standardisation organisations the european committee for standardization , the european committee for electrotechnical standardization and the european telecommunications standards institute; ### 104. calls for the creation of an adequately resourced mechanism to supervise the uniform , eu-wide implementation and enforcement of the upcoming ai laws;


### 105. highlights that the underlying objective of the eu’s digital strategy , as well as that of the ai strategy is to create a ‘european way’ in a digitalised world; clarifies that this approach should be human-centred , value-oriented and based on the concept of the social market economy; underlines that the individual , with their respective dignity and individual freedoms , should always remain at the centre of all political considerations; ### 105. highlights that an underlying objective of the eu’s digital strategy , as well as that of the ai strategy is to create a ‘european way’ in a digitalised world; clarifies that this approach should be human-centred , value-oriented and based on the concept of the social market economy; underlines that the individual , with their respective dignity and individual freedoms , should always remain at the centre of all political considerations;


### 107. states that the co-legislators should aim to align the ai definition in future legislation with the concepts , terminologies and standards developed together with other like-minded democratic countries in the oecd44 ; stresses that doing so would give the eu an advantage in shaping a future international ai governance system; ### deleted


### 108. is convinced that it is not ai as a technology that should be regulated , but that the type , intensity and timing of regulatory intervention should solely depend on the type of risk incurred by the use of an ai system; underlines , in this regard , the importance of distinguishing between a minority of ‘high-risk’ and the vast majority of ‘low-risk’ ai use cases; concludes that while only the former category indeed demands legislative safeguards , businesses should self-regulate ‘low-risk’ technologies by choosing measures that deliver the best outcomes; ### deleted


### 109. specifies that the classification of technologies as ‘high-risk’ should be based on the concrete use and context , complexity and autonomy of the ai system , the probability and likelihood of the worst-case scenario , the severity of the harm and its irreversibility , the techniques used and the governance arrangements adopted; stresses that this classification should be introduced together with best practices and guidance for ai developers and should also recognise that ai technologies can significantly reduce certain risks; ### deleted


### 110. notes that the requirements that ai systems need to fulfil differ significantly in a business-to-business (b2b) environment compared to a business-to-consumer (b2c) environment; points out that while consumer rights need to be legally protected through consumer protection legislation , companies can solve liability and other legal challenges more quickly and cost-effectively by contractual means with business partners directly; concludes that , in particular , smes and start-ups investing in ai technologies would benefit from a b2b exclusion as they are disproportionately affected by new legal obligations , which also harms their ability to attract investments; ### 110. notes that the requirements that ai systems need to fulfil differ significantly in a business-to-business (b2b) environment compared to a business-to-consumer (b2c) environment; points out that consumer rights need to be legally protected through consumer protection legislation; stresses that , while companies can solve liability and other legal challenges quickly and cost-effectively by contractual means with business partners directly , legislation may be necessary to protect smaller businesses from market power abuse by dominant actors through commercial or technological lock-in , barriers to market entry or asymmetric information problems; highlights that there is also a necessity not to overburden smes and start-ups investing in ai technologies with overly complex legislative requirements , which would put them at a disadvantage with larger companies , which have the resources to maintain sizeable legal and compliance departments;


### 111. underlines the need to address open ethical questions raised by new technological possibilities , but clarifies that new ai ethical guidelines should not set up stricter rules than those already existing for human or automated actions; proposes that on these grounds the eu should introduce ethical guidelines that consist of three categories of core values and principles; ### 111. underlines the need to apply a principles based approach to open ethical questions raised by new technological possibilities resulting from the sale and use of ai applications , including through the use of fundamental , mandatory principles such as the non-maleficence principle , the principle of respecting human dignity and fundamental rights , or the protection of the democratic process; notes that good practices in ai development such as human-centric ai , responsible governance and the principles of transparency and explainability , as well as principles of sustainable ai that would be fully aligned with the un 2030 agenda for sustainable development are other important components in shaping the ai economy;


### 112. explains that the first category could list fundamental , mandatory principles such as the non-maleficence principle , the principle of respecting human dignity or the protection of the democratic process; states that the second category could include good practices in ai development such as human-centric ai , responsible governance and the principles of transparency and explainability; concludes that the last category could include principles of sustainable ai that would be fully aligned with the un 2030 agenda for sustainable development; ### deleted


### 113. highlights , with regard to the third category , the gap in leadership on ai global governance , which gives the eu the chance to become the leading voice in aligning ai with the un sdgs and using ai technologies to push worldwide for their achievement; stresses , however , that not all ai technologies developed or applied in the eu should need to comply with all three categories; suggests , for example , that sustainable ai could only be mandated for ai implemented or procured by public tender or in specific sectors , while the majority of ai developers and companies would only be encouraged to align with the second and third categories through soft law; ### deleted


### 114. is convinced that efforts to completely ‘de-bias’ ai algorithms are frequently misguided , because this strategy wrongly suggests that bias-free data sets exist; notes that in this regard the requirement that data used to train ai systems is ‘complete and free of errors’ needs to be revisited; stresses , however , that the eu should at the same time cooperate very closely with ai developers to counterbalance structural biases in our societies and daily life; ### 114. is convinced that efforts to completely ‘de-bias’ ai algorithms are frequently misguided , because this strategy wrongly suggests that bias-free data sets exist; stresses , however , that the eu should at the same time cooperate very closely with ai developers to counterbalance structural biases in our societies and daily life;


### 115. elaborates that transparency or explainability obligations for ai systems , while helpful in certain cases , may not be possible to implement in every instance; notes that both concepts also need to be balanced against other factors , including the interests of businesses in maintaining trade secrets or the potential value of exposed data to potential competitors; stresses , however , that a mandatory self-identification of ai systems or accessible machine logs seem to be very useful for many ai use cases that interfere with the fundamental rights of individuals or affect consumers; ### 115. elaborates that meaningful transparency or explainability obligations for ai systems , while helpful in many cases , may not be possible to implement in every instance;


### 116. states that the legislative framework on intellectual property must continue to incentivise and protect ai innovators by granting them patents as a reward for developing and publishing their creations; finds that existing laws are mostly future-proof , but proposes certain adjustments , including the integration of open source elements and new forms of patent licensing to ensure that tools are available to regions and initiatives that could not otherwise afford them; recognises that it will also be necessary to clarify whether ai will be able to hold intellectual property rights in itself; ### 116. proposes new forms of compulsory patent licensing to ensure that tools are available to regions and initiatives that could not otherwise afford them;


### 117. elaborates that obligatory ex ante risk self-assessments , comparable with ce markings or data protection impact assessments , combined with market surveillance based on clear rules and standards , and complemented with ex post enforcement for high-risk ai systems , seem to be a sufficiently robust governance approach for ai; warns that overly burdensome conformity assessment obligations could create significant burdens that make the business models of ai developers and companies economically unviable; ### deleted


### 118. notes that in order to increase product safety and improve the identification of faults , the developers of high-risk ai should at least be obliged to ensure that accessible logs of algorithmic activity are maintained securely; considers that developers should also design high-risk ai systems with embedded mechanisms – ‘kill switches’ – for human intervention to immediately halt automated activities at any moment; ### deleted


### 119. is convinced that despite the legal challenges caused by ai systems , there is no need for a complete revision of the existing liability rules; stresses that the product liability directive and the national fault-based liability regimes can in principle remain the centrepiece legislation for countering most harm caused by ai; underlines that only in some cases could there be inappropriate outcomes , but warns that any revision should take the existing product safety legislation into account and should solely be based on clearly identified gaps; ### 119. recognises the legal challenges caused by ai systems , and that there is a need to consider a revision of the existing liability rules; carefully considers whether national fault-based liability regimes , can remain the centrepiece legislation for countering most harm caused by ai; underlines that in some cases there could be inappropriate outcomes , but warns that any revision should take the existing product safety legislation into account , while being future proof and capable of being effectively implemented and ensuring protection of individuals in the eu; stresses that harm resulting from insufficient allocation of resources or lack of care provision by means of ai recommender systems in the healthcare sector should be addressed in any future regulatory reform;


### 120. notes that certain changes to the legal definitions of ‘product’ , including integrated software applications , digital services and inter-product dependency , and ‘producer’ , including backend operator , service provider and data supplier , do however seem necessary to ensure that compensation is available for harm caused by emerging technologies; stresses , however , that an overly broad approach to the definition of ‘product’ should be avoided , as this may make it difficult to differentiate between ai and other algorithms; ### 120. notes that certain changes to the legal definitions of ‘product’ , including integrated software applications , digital services and inter-product dependency , and ‘producer’ , including backend operator , service provider and data supplier , do however seem necessary to ensure that compensation is available for harm caused by these technologies; stresses , however , that an overly broad or excessively narrow approach to the definition of ‘product’ should be avoided;


### 121. points out that , due to the characteristics of ai systems , such as their autonomy and opacity , there could also be cases where neither an updated product liability directive nor national fault-based liability regimes apply and where persons who suffer harm or whose property is damaged would end up without compensation; suggests , therefore , the introduction of a limited new liability mechanism for legal claims against the operator , who controls the risks associated with the ai system and who also often is the cheapest cost avoider; specifies that while high-risk ai systems should fall under strict liability , combined with mandatory insurance cover , victims of low-risk ai systems should only benefit from a presumption of fault against the operator; ### 121. points out that , due to the characteristics of ai systems , such as their autonomy and opacity , there could also be cases where neither an updated product liability directive nor national fault-based liability regimes apply and where persons who suffer harm or whose property is damaged would end up without compensation; suggests to clarify legal claims against the operator , who controls the risks associated with the ai system;


### 122. agrees with the conclusion drawn by the commission in its 2020 communication entitled ‘a european strategy for data’ that the creation of a single european data space is key to ensuring the eu’s global competitiveness in ai , as well as its strategic sovereignty and economic prosperity; recalls the essential link between the availability of high-quality data and the development of ai; ### 122. agrees with the conclusion drawn by the commission in its 2020 communication entitled ‘a european strategy for data’ that the completion of the digital single market and the creation of horizontal or sector-specific european data spaces , and a focus on common standards are key to ensuring fast scalability of ai solutions in the eu and beyond; ; recalls the essential link between the availability of high-quality data and the development of ai;


### 123. calls on the member states to review their national ai strategies that they developed in accordance with the ‘coordinated plan on ai’ , as the vast majority of them remain vague and lack clear goals; recommends that they formulate more concrete , quantifiable and specific actions , while trying to create synergies between them; ### 123. calls on the member states to review their national ai strategies , as the vast majority of them remain vague and lack clear goals; recommends that they formulate more concrete , quantifiable and specific actions , while trying to create synergies between them;


### ii. market barriers ### deleted


### 124. urges the commission to continue its work on removing key barriers for developers and companies in areas such as country-based discrimination , burdensome market access procedures and high regulatory costs , as well as to address the frequent use of derogations which results in diverging rules among different member state jurisdictions; ### deleted


### 127. stresses that the shift in the volume and processing of data for ai requires the development and deployment of new data processing technologies encompassing the edge , thereby moving away from centralised cloud-based infrastructure models towards increasing decentralisation of data processing capacities; urges the strengthening of european intense-computing ai architectures as a key strategic priority to maximise investment and research , including distributed clusters , the deployment of edge nodes , digital microcontroller initiatives , and the capacity to enable faster data collection and processing in all aspects of society; ### 127. stresses that the shift in the volume and processing of data for ai requires the development and deployment of new data processing technologies encompassing the edge , thereby moving away from centralised cloud-based infrastructure models towards increasing decentralisation of data processing capacities; highlights that moving from a centralized infrastructure to a generalisation of edge solutions will lose the benefit of the pooling optimisation effect and will be more resource intensive; stresses that the environmental cost/benefit of any edge infrastructure should be looked into at a systemic level in a european cloud strategy; highlights that unnecessary data acquisition and processing should be identified and eliminated in order to optimise energy consumption of ai;


### 128. stresses that ai requires powerful hardware to make sophisticated algorithms useable , including high-performance and quantum computing and the iot; urges the maximisation of funding and research for such ai-enabled emerging technologies; finds that , similarly , nano-technologies and chips are essential to enabling ai to be embedded in , for example , medical devices , which also requires priority funding; ### 128. stresses that ai requires powerful hardware to make sophisticated algorithms useable , including high-performance and quantum computing and the iot; calls for continued increases in funding for research for ai-enabled emerging technologies and solutions to reduce the energy consumption , including software eco design.; highlights that 30% of running servers are currently unused due to the ease of development of virtual servers and the lack of standards and accountability for the use of resources; calls for the development of standards on measurement at eu level based on best practices;


### 129. highlights that a functioning and fast infrastructure for ai must be based on a fair , safe and high-quality foundation by avoiding gaps in digital high-speed connectivity , which requires 5g roll-out in all urban areas by 2030 , as well as ultra-fast broadband networks and spectrum policy with licence conditions that do not distort competition; urges member states to continue to implement the 5g toolbox , specifically enabling legislation related to the risk assessment of suppliers and service providers; calls for the broadband cost reduction directive to be put into practice to facilitate network deployment; ### deleted


### 130. calls on the commission to establish timetables and financial incentives for member states , cities , regions and industry , and to accelerate the administrative approval processes for 5g; supports the incentivisation of private investment in 5g roll-out; requests that in regions where roll-out is not carried out by the private sector , more funds are made available; calls for funding for broadband and connectivity projects under the multiannual financial framework , with easier access for local authorities to avoid the underutilisation of public funds; ### deleted


### 131. calls on the commission to establish a precise strategy with a clear timetable for 6g roll-out to better prepare for the next wave of digital infrastructure , enabling europe to take the lead; ### 131. calls on the commission to assess the interplay between ai and the next wave of digital infrastructure , enabling europe to take the lead in next generation networks;


### 132. finds that it will not be possible to achieve the necessary deployment of dense edge-node connectivity for 5g in rural areas , where half of european households are not even connected through fibre; calls for a clear strategy on fibre-optic network deployment and broadband roll-out in rural areas , which is also key for data intensive technologies such as ai; recommends that european investment bank support for connectivity projects in rural areas be enhanced; ### deleted


### 133. stresses that the significant investment required for network deployment , coupled with the ambitious expectations of public authorities and consumers regarding roll-out timing and coverage , will be impossible to achieve without infrastructure-sharing agreements , which are also key to promoting sustainability and reducing energy consumption; ### deleted


### 134. urges the eu to take the lead in making green digital infrastructure climate neutral and energy efficient by 2030; calls for coordinated global multilateral action to use ai in the fight against climate change and environmental degradation; ### 134. taking into account the increased energy needs of ai development and use , urges the eu to take the lead in making green digital infrastructure climate neutral and energy efficient by 2030 in line with the paris agreement targets; calls for coordinated global multilateral action in the fight against climate change , environmental and ecological degradation , as well as biodiversity loss , noting that ai can be an enabler for the solutions;


### 135. calls on the commission to create an ai competence framework for individuals that builds on the digital competence framework for citizens , which helps individuals and smes to find relevant ai training and learning opportunities and to improve the sharing of knowledge , best practices , digital skills initiatives and funding between organisations and companies , at both eu and national level; recommends the establishment of a central body for the european ai skills data space to coordinate european skills training on sectoral and regional levels in all member states; urges the commission and the member states to support free online courses that enhance digital literacy such as basic training in ai; ### 135. calls on the commission to create an ai competence framework for individuals that builds on the digital competence framework for citizens , which helps individuals and smes to find relevant ai training and learning opportunities and to improve the sharing of knowledge , best practices , digital skills initiatives and funding between organisations and companies , at both eu and national level; urges the commission and the member states to support free online courses that enhance digital literacy such as basic training in ai;


### 137. encourages all member states to spend a significant proportion of their gdp on research into digital technologies , and for annual combined public and private investments in the eu to reach at least eur 20-25 billion; urges the continued strengthening of the horizon europe programme , notably its ai , data and robotics partnership and the european innovation council , and to expand the digital europe programme , whose allocated funding of eur 7.6 billion46 is insufficient to remain competitive; ### 137. encourages all member states to spend a significant proportion of their gdp on research into digital technologies; urges the continued strengthening of the horizon europe programme , notably its ai , data and robotics partnership , and to expand the digital europe programme;


### 138. calls on the commission to simplify and streamline the structure of research funding instruments by reducing the effort and time needed to obtain decisions when applying for grants; stresses the need to improve the quality and consistency of proposal reviews and to increase the predictability of funding instruments and their timing to support long-term planning , using the european ai research roadmap; ### 138. calls on the commission to simplify and streamline the structure of research funding instruments by reducing the effort and time needed to obtain decisions when applying for grants; stresses the need to improve the quality and consistency of proposal reviews and to increase the predictability of funding instruments and their timing to support long-term planning , using the european ai research roadmap; stresses however the need to prohibit eu-funding of programmes that pose an unacceptable risk to fundamental rights , such as the funding of biometric systems for mass surveillance for new or existing projects (bodega or smile) or research-funding of ai technologies that contribute to environmental harm , in particular in machine-learning and distributed ledger technology;


### 139. encourages the creation of more chairs on ai at european universities as well as competitive salaries for ai research and the provision of more funding in order to properly train and retain the next generation of researchers and entrepreneurs and prevent brain drain to locations outside the eu; stresses the need to reduce the bureaucratic burden for university researchers in accessing funds and calls on the commission to provide tools to increase digital interconnectivity between universities; urges the development of cross-cutting networks for ai across european universities and research institutions; ### 139. encourages the creation of more chairs on ai at european universities as well as competitive salaries for ai research and the provision of more funding in order to properly train and retain the next generation of researchers and entrepreneurs; stresses the need to reduce the bureaucratic burden for university researchers in accessing funds and calls on the commission to provide tools to increase digital interconnectivity between universities; urges the development of cross-cutting networks for ai across european universities and research institutions;


### 27. highlights that ai applications can bring environmental and economic benefits and strengthen predictive capabilities that contribute to the fight against climate change , to meeting the sustainable development goals (sdgs) and to achieving our target of becoming the first climate-neutral continent; finds that the use of ai has the potential to reduce global greenhouse gas emissions by up to 4 % by 203022 ; underlines that ai systems themselves need to be designed sustainably to reduce resource usage and energy consumption , thereby limiting the risks to the environment; finds that it has been estimated that ict technologies are capable of reducing 10 times more greenhouse gas emissions than their own footprint23 ; ### 27. highlights that the european commission’s two key priorities for the upcoming years are europe fit for the digital age and the green deal; underlines the need to ensure that the digital transition promotes and does not hamper the green transition; finds that this require an acceleration of innovation and digitalisation in a way compatible with the eu’s climate neutrality target , co2 reduction goals , and high environmental standards; recalls that the commission stated in communication com(2020)67 that “the environmental footprint of the sector is significant , estimated at 5-9% of the world’s total electricity use and more than 2% of all emissions.”; emphasises that addressing the environmental impact of the ict sector can only be done with the relevant information and data to make digital technologies and infrastructures , such as data centres , sustainable;


### 28. is concerned that only six member states have included a strong focus on ai applications in their efforts to meet the green deal objectives; finds that ai will produce information relevant to environmental planning , decision-making and the management and monitoring of the progress of environmental policies , for instance for cleaner air , where ai applications can monitor pollution and warn of hazards; highlights that ai and digital solutions have the potential to scale up resource-efficient solutions that would otherwise only be implemented in one company or sector; ### 28. is concerned that only six member states have included a strong focus on ai applications in their efforts to meet the green deal objectives; finds that ai can be used to collect and organize information relevant to environmental planning , decision-making and the management and monitoring of the progress of environmental policies , for instance for cleaner air;


### 29. emphasises the importance of ai in developing smart cities and smart villages to improve the technological resilience of infrastructures , building on local strengths and opportunities , including public transport , emergency assistance , waste management , urban planning , smart energy and resource optimisation; stresses that ai-based solutions can further assist in optimising architecture , construction and engineering processes to reduce emissions , construction time , costs and waste; finds that this is already a reality in countries such as china and malaysia , where large-scale urban ai systems manage the transport , energy and safety systems of several cities; ### 29. emphasises the importance of ai in developing solutions , including public transport , emergency assistance , waste management , urban planning , smart energy and resource optimisation; stresses that ai-based solutions can further assist in optimising architecture , construction and engineering processes to reduce emissions , construction time , costs and waste;


### 30. stresses that the energy transition will not take place without digitalisation , which will be achieved to a significant extent through ai; underlines that ai can monitor , optimise and reduce energy consumption and production , as well as support the integration of renewable energies into existing electricity grids; ### 30. stresses that the energy transition will not take place without digitalisation , ; underlines that ai can monitor , optimise and reduce energy consumption and production , as well as support the integration of renewable energies into existing electricity grids;


### 31. highlights that the growing complexity of an energy transition system , with increased volatile renewable generation and changes in load management , makes increasing automated control necessary for energy supply security; stresses that ai benefits for supply security , especially in the operation , monitoring , maintenance and control of water , gas and electricity networks , must be taken into account in the regulation of these networks; ### 31. highlights that the growing complexity of an energy transition system , with increased volatile renewable generation and changes in load management , makes increasing automated control necessary for energy supply security; stresses that ai has the potential to benefit supply security , especially in the operation , monitoring , maintenance and control of water , gas and electricity networks;


### 32. finds that ai and other digital solutions for mobility and transport have the potential to reduce traffic flows and enhance road safety , by greatly increasing the efficiency of access to transport systems through , for example , autonomous vehicles and optimised public transport planning , thus reducing the environmental footprint of the transport sector , travel times and costs; ### 32. finds that ai and other digital solutions for mobility and transport have the potential to optimise traffic flows and enhance road safety , including by increasing the efficiency of access to transport systems;


### 33. believes that ai can have a transformative role in the agricultural sector when it comes to tackling food security issues , enabling the emergence of new harvesting methods and harvest prediction , novel approaches to food processing and retail , agricultural resource management and input efficiency , as well as improved land management and the optimisation of food supply chains; stresses that agriculture is a key sector in which ai can help to cut emissions and the use of pesticides , fertilisers , chemicals and water; further stresses that ai can contribute to the restoration of biodiversity and prevent deforestation by monitoring endangered species and tracking deforestation activities through smart forests; ### 33. believes that ai can have a transformative role in the agricultural sector by enabling the emergence of new harvesting methods and harvest prediction , novel approaches to food processing and retail , agricultural resource management and input efficiency , as well as improved land management and the optimisation of food supply chains; stresses that agriculture is a key sector in which ai can help to cut emissions and the use of pesticides , fertilisers , chemicals and water; further stresses that ai can contribute to the restoration of biodiversity by monitoring endangered species or tracking deforestation activities;


### 34. stresses that ai contributes to a circular economy through increased production output and quality , reduced maintenance costs , better use and the ethical sourcing of raw materials , and reduced waste; highlights that ai has the potential to automatically provide businesses with detailed insight into their emissions , including value chains , and forecast future emissions , thus helping to adjust and achieve individual emission targets; underlines that digital tools help businesses to implement the necessary steps towards more sustainable conduct , especially small and medium-sized enterprises (smes) which do not otherwise have the resources to do so; ### 34. stresses that ai can contribute to a circular economy by rendering production , consumption and recycling processes and behaviour more resource efficient and increasing the transparency of material use , for example with regards to the ethical sourcing of raw materials and reduced waste; highlights that ai has the potential to increase business' understanding of their emissions , including in value chains thus helping to adjust and achieve individual emission targets;


### 35. stresses that more environmental data is needed in order to gain more insight and induce more progress through ai solutions; underlines that using ai to systematically connect data on co2 emissions with data on production patterns , consumer behaviour , supply chains and logistics routes could ensure that activities that have a positive or negative impact are detected; ### 35. highlights that to date , it is not yet possible to fully measure the environmental impacts generated using artificial intelligence; points out that cases of ai used to reduce environmental impacts are still rare; is concerned that only few studies have been published; underlines that using ai to systematically connect data on co2 emissions with data on production and consumption patterns supply chains and logistics routes could ensure that activities that have a positive or negative impact are detected;


### c) external policy and the security dimension of ai ### c) the international dimension of ai


### 36. is concerned that the global community does not seem likely to reach an agreement on minimum standards for the responsible use of ai , as the stakes , in particular for the most powerful nations , are too high; believes , however , as a matter of principle , in the potential of democratic nations to jointly shape the international debate , to work together towards certain minimum standards , and thereby to promote multilateralism , interoperability and data sharing on the international stage; ### 36. welcomes the work ongoing at the international level on ai; considers it of paramount importance for the international community to reach an agreement on minimum standards for the responsible use of ai; highlights the key role the eu can play in this sense , as the first bloc in the world to introduce legislation on ai; welcomes the fact that like-minded democracies are working together to jointly shape the international debate and to agree on common principles , and thereby to promote multilateralism , interoperability and data sharing on the international stage;


### 37. observes that chinese nationals have assumed leadership positions in the international organization for standardization , the international electrotechnical commission and the international telecommunication union , the three largest and best-established standard-setting organisations in the world , while the chinese government has also signed standards and cooperation agreements with 52 other countries through its belt and road initiative; warns that since several of their promoted standards , including on ai technologies and in particular in relation to government surveillance and individual liberties , are not in line with eu values , the chinese standard offensive poses a crucial geopolitical challenge for the eu , while also giving china a first-mover advantage in economic terms; ### 37. observes that the chinese government has also signed standards and cooperation agreements with 52 other countries through its belt and road initiative; warns that several of their promoted standards , including on ai technologies and in particular in relation to government surveillance and individual liberties , are not in line with eu values;


### 38. stresses that ai technologies , used in military command centres or in missile launch facilities , could escalate an automated reciprocal conflict before humans have the chance to detect what is happening , understand the causes and intervene; agrees with studies that warn that the impact of ai technologies on warfare could rival that of nuclear weapons24 ; ### 38. stresses that ai technologies , used in military command centres or in missile launch facilities , could escalate an automated reciprocal conflict before humans have the chance to detect what is happening , understand the causes and intervene;


### 39. notes that the use of ai systems in defence-related developments is considered a game changer in military operations; states that the key advantage lies in the potential to engage in armed conflicts with a reduced risk of physical harm to one’s own military personnel and as a means to reduce military response time; ### 39. notes that the use of ai systems in defence-related developments is considered a game changer in military operations; states that the key danger lies in the potential to engage in armed conflicts with a reduced risk of physical harm to one’s own military personnel;


### 40. is concerned about military research and technological developments relating to lethal offensive weapon systems without human oversight that are pursued in countries such as russia and china with little regard for the risk to humanity; observes that such lethal offensive weapon systems are already used in military conflicts; warns that even non-state armed groups could soon equip drones with ai software for navigation and facial recognition and thus turn them into cheap lethal offensive weapons capable of acting entirely without human oversight; ### 40. is concerned about military research and technological developments relating to lethal autonomous weapon systems (laws); observes that such lethal offensive weapon systems are already used in military conflicts; is concerned about the risk of non-state groups coming in possession of such weapons;


### 41. notes that ai technology can also be used as a means for various forms of hybrid warfare; specifies that it could for instance be mobilised to trigger information warfare , by using fake social media accounts , to weaponise interdependence , by gathering valuable information or denying network access to adversaries , or to create disturbances in the economic and financial systems of other countries; ### 41. notes that ai technology can also be used as a means for various forms of hybrid warfare; specifies that it could for instance be mobilised to trigger information warfare;


### 42. illustrates that ai technologies could also help perpetrators by simplifying the use of very sophisticated cyberattacks , such as through ai-powered malware , identity theft using biometric data or adversarial ai that causes other ai systems to misinterpret input; points , in particular , to the rise in deepfakes , which already lead to doubts about the authenticity of all digital content , including genuinely authentic videos; warns that deepfakes could contribute to a broad climate of public mistrust in ai , as well as a deeper socio-political polarisation within our societies; ### 42. illustrates that ai technologies could also include ai-powered malware , identity theft , data poisoning , or adversarial ai that causes other ai systems to misinterpret input; points , in particular , to the rise in deepfakes , which already lead to doubts about the authenticity of digital content; warns that deepfakes could contribute to a broad climate of public mistrust in ai , as well as a deeper socio-political polarisation within our societies;


### 43. elaborates that the internet of things , as well as the fact that ai systems nowadays run a significant amount of key critical infrastructure , such as energy grids , the food chain , the atm network and hospital logistics , has created a massive ai cybersecurity threat; predicts that states will focus more and more on protecting their it logistics and care delivery as a domestic asset , which could in turn create the temptation to invoke ‘ai autarchy’; ### 43. elaborates that ai systems are developed to be deployed in a significant amount of key critical infrastructure , such as energy grids , the food chain , the atm network and hospital logistics , which creates serious ai cybersecurity threats; points out that a silo mentality and a security through obscurity approach need to be avoided;


### 44. explains that the high level of accuracy that ai can achieve may pose security risks , as it can induce humans to place such confidence in ai as to trust it more than their own judgment; notes that experiments have shown that this can elevate the level of autonomy of ai beyond the supporting role for which it was originally designed and means that humans miss opportunities to gain experience and refine their skills and knowledge of ai systems; observes that this type of ai overuse has , for example , been cited as a major factor in several aircraft crashes25 ; ### 44. points to the risk of humans placing such confidence in ai as to trust it more than their own judgment; notes , therefore , the limits of “human in the loop” as a corrective mechanism; notes that experiments have shown that this can elevate the level of autonomy of ai beyond the supporting role for which it was originally designed and highlights the need for education and digital skills; observes that over-dependence on technology has , for example , been cited as a major factor in several aircraft crashes25;


### 45. highlights however that ai’s core characteristics also make the technology an ideal tool to enhance security; specifies that it can be used to synthesise large amounts of data , perform behavioural analysis of network activities and detect specific patterns; stresses that these elements would allow for better prediction and assessment of the threat level , faster decision-making processes , improved reactivity and the more effective securing of endpoint devices; ### 45. highlights however that ai systems can synthesise large amounts of data , and perform behavioural analysis of network activities; stresses that these elements would allow for better prediction and assessment of the threat level and system vulnerabilities , faster decision-making processes , improved reactivity and the more effective securing or exploiting of endpoint devices;


### 46. underlines , in particular , the potential inherent in enabling law enforcement agencies to proactively assess and predict ai misuse , as well as to counter it effectively by using ai technologies themselves; underlines that such ai-supported law enforcement activities do , however , require clear transparency rules , highly skilled employees and access to large amounts of relevant data; ### 46. underlines the importance to enable law enforcement agencies to identify and counter criminal activity , which is aided by ai technology; underlines that ai-supported law enforcement activities do , however , create substantial risks to the right to a fair trial , to freedom of expression , to freedom of assembly , and other fundamental rights; require therefore a precautionary approach , clear rules and highly skilled employees;


### 63. acknowledges at the same time that the very same ai technologies used to address fundamental societal problems and achieve important public policy goals could also pose crucial ethical and legal questions; notes that there have already been documented instances which have led to serious wrongdoing within the eu; notes that in practice , member states rely heavily on the police to collect data , but also on private entities whose activities are often not supervised and who regularly sell the collected and highly sensitive data to other third parties; clarifies that this practice runs counter to european values and undermines the high level of fundamental rights in the eu , in particular the right to privacy; ### 63. notes with concern that the very same ai technologies used to address fundamental societal problems and achieve important public policy goals also pose crucial ethical and legal questions; notes that in practice , member states rely heavily on the police to collect data , but also on private entities whose activities are often not supervised and who regularly sell the collected and highly sensitive data to other third parties; clarifies that this practice runs counter to european values and undermines the high level of fundamental rights in the eu , in particular the rights to privacy and data protection;


### 64. stresses that many authoritarian regimes apply ai systems to control , spy on , monitor and rank their citizens; believes that any form of unrestricted normative citizen scoring on a large scale by public authorities , especially within the field of law enforcement and the judiciary , leads to the loss of autonomy and is not in line with european values; highlights past cases of eu companies having sold biometric systems which would be illegal to use within the eu to authoritarian regimes in non-eu countries; ### 64. stresses that many authoritarian regimes apply ai systems to control , spy on , monitor and rank their citizens; believes that any form of normative citizen scoring by public authorities , especially within the field of law enforcement and the judiciary , is not in line with european values; therefore calls for a ban of for the use of ai in the judiciary for delivering or proposing decisions; highlights past cases of eu companies having sold biometric systems which would be illegal to use within the eu to authoritarian regimes in non-eu countries;


### 65. notes that dominant tech platforms nowadays not only have significant control over access to information and its distribution , but they also use ai technologies to obtain more information on a person’s identity and knowledge of decisional history than is possessed by public authorities or close personal advisors such as doctors , lawyers or bankers; stresses that this development challenges the sovereignty of our nation states , the foundations of our democratic systems and the safeguarding of our fundamental rights; ### 65. notes that dominant tech platforms nowadays not only have significant control over access to information and its distribution , but they also use ai technologies to obtain more information on a person’s identity and knowledge of decisional history; stresses that this development challenges the sovereignty of our nation states , the foundations of our democratic systems and the safeguarding of our fundamental rights;


### 67. underlines that ai could , however , also be used to reduce anti-democratic and unethical activities on platforms and as a means to stop the distribution of fake news; notes that the effective use of ai for this purpose has so far been prevented by strongly diverging definitions of hate speech among member states and the lack of consensus on how to harness ai to effectively filter out illegal and harmful content; explains that it is also problematic that divisive language leads to greater user engagement , which is why removal of such language would be in direct conflict with the platform’s business model , based on maximising user engagement; ### 67. underlines that ai could , however , also be used to reduce anti-democratic and unethical activities on platforms , assisting in limiting the distribution of fake news; notes however that due to its inability to understand context-specific content ai cannot effectively be used to detect hate speech; explains that it is problematic that divisive language leads to greater user engagement , which is why removal of such language would be in direct conflict with the platform’s business model , based on maximising user engagement;


### 68. stresses that bias in ai systems often occurs due to a lack of diverse and high-quality training data , for instance where data sets are used which do not sufficiently cover discriminated groups , or where the task definition or requirement setting themselves were biased; notes that bias can also arise due to a limited volume of training data , which can result from overly strict data protection provisions , or where a biased ai developer has compromised the algorithm; points out that some biases in the form of reasoned differentiation are , on the other hand , also intentionally created in order to improve the ai’s learning performance under certain circumstances; ### 68. stresses that bias in ai systems often occurs due to a lack of diverse and high-quality training data , for instance where data sets are used which do not sufficiently cover discriminated groups , or where the task definition or requirement setting themselves were biased; notes that bias can also arise due to a limited volume of training data , or where a biased ai developer has compromised the algorithm; points out that some biases in the form of reasoned differentiation are , on the other hand , also intentionally created in order to improve the ai’s learning performance under certain circumstances;


### 69. raises the question of whether certain biases can be resolved by using more diverse datasets , given the structural biases present in our society; specifies in this regard that algorithms learn to be as discriminatory as the society they observe and then suggest decisions that are inherently discriminatory , which again contributes to exacerbating discrimination within society; concludes that there is therefore no such thing as a completely impartial and objective algorithm; ### 69. raises the question of whether certain biases can be resolved by using more diverse datasets , given the structural biases present in our society;; stresses that one of the most efficient ways of reducing bias in ai systems is to ensure , to the extent possible under union law , that the maximum amount of non-personal data is available for training purposes and machine learning; calls on the commission to reflect on the use of public domain data for such purposes; concludes that there is therefore no such thing as a completely impartial and objective algorithm;


### 70. believes that that the adoption of ai , if combined with the necessary support infrastructure and training , can substantially increase productivity , innovation , growth and job creation , with expectations of labour productivity set to increase by 11-37 %26 by 2035; ### 70. notes that ai is increasingly influencing the labour market , the workplace and the social domain; the impacts of technological change on work and employment are multifaceted; believes that that the adoption of ai , if combined with the necessary support infrastructure and training , can substantially increase productivity , innovation , growth and job creation; emphasises that in the area of employment , the use of ai gives rise to a number of ethical , legal and employment related challenges; is concerned that in terms of the labour market , digitalisation will lead to workforce reorganisation and potential disappearance of certain sectors of employment;


### 71. stresses that although ai may replace some tasks , including mundane , labour-intensive or dangerous tasks , it will create new , higher value-added employment; stresses that ai is currently substituting or complementing humans in a subset of tasks but that it is not yet having detectable aggregate labour market consequences27 ; stresses , however , the potential for an increase in income inequality if ai is augmenting high-skill occupations and replacing low-skill occupations , and that such possible effects need to be prepared for; ### 71. stresses that ai may replace some tasks , including mundane , labour-intensive or dangerous tasks , it may also may help to improve skills , raise the quality of work and create new , higher value-added employment; is concerned ai can produce processes of deskilling and create and embed low paid , low autonomy work and extend atypical , flexible (or 'gig') work; stresses that ai is currently substituting or complementing humans in a subset of tasks but that it is not yet having detectable aggregate labour market consequences27 ; stresses , however , the potential for an increase in income inequality if ai is augmenting high-skill occupations and replacing low-skill occupations , and that such possible effects need to be prepared for;


### 72. highlights that ai implementation also represents an opportunity for significant cultural change within organisations , including improved workplace safety , better work-life balance and more effective training and guidance; is of the opinion that human-empowering ai applications could also create new job opportunities , in particular for those who , because of natural restrictions such as disabilities or living circumstances , were initially bound to less qualified jobs; ### 72. highlights that ai implementation also represents an opportunity for significant cultural change within organisations , and more effective training and guidance; is of the opinion that human-empowering ai applications could also create new job opportunities , in particular for those who , because of natural restrictions such as disabilities or living circumstances , were initially bound to less qualified jobs;


### 74. considers that the adaptation of the workforce in terms of ai education and retraining is of vital importance , as 52 %28 of the current european workforce urgently requires retraining; highlights that current concepts of learning and working are still defined to too great an extent by the job market needs of a pre-digital world , which also contributes to a growing skills gap and a new digital divide for both citizens and businesses who do not have access to a secure digital space; stresses that enhancing digital literacy contributes to achieving the sdgs , in particular those on education , human capital and infrastructure; ### 74. considers that the adaptation of the workforce in terms of ai education and retraining is of vital importance; highlights that current concepts of learning and working are still defined to too great an extent by the job market needs of a pre-digital world , which also contributes to a growing skills gap and a new digital divide for both citizens and businesses who do not have access to a secure digital space; stresses that enhancing digital literacy contributes to achieving the sdgs , in particular those on education , human capital and infrastructure;


### 76. notes that there are a number of transversal obstacles that the eu needs to overcome in order to achieve a widespread use of ai and to fully harness its benefits; states that in particular , legal uncertainty , insufficient digital infrastructure and a lack of ai skills can be observed as barriers to the successful application of ai in all fields analysed; ### 76. notes that there are a clear benefits and opportunities from the adoption of ai technologies for the entire society which can only be reaped if transversal obstacles that are addressed in the eu; states that risks to fundamental rights , legal uncertainty and a lack of accessible digital infrastructure and digital skills in the broader society can be observed as barriers to the successful application of ai in all fields analysed;


### 77. concludes from the case studies examined , furthermore , that it is not specific ai technologies themselves that are risky , but certain use cases; points in particular to dual-use ai systems such as drones , the uses of which can vary drastically from consumer recreation to warfare , with the worst-case scenario being swarms of inexpensive , armed microdrones used to kill specific human targets; ### 77. concludes from the case studies examined , furthermore , that there are specific aspects of the development of ai technologies that are risky or harmful in certain use cases; recognizes that future regulation needs to address the legitimate concerns in light of this risk , in order for ai technologies to find broads application in the eu;


### 78. states that while it is important to examine and categorise potential risks posed by ai , the case studies illustrated that ai technologies also enable us to apply , in most cases , very effective counter measures that are able to mitigate or eliminate the very same risks; underlines that , as ai is still in its early stages of development within a wider context of emerging technologies , its real potential can still only be imagined; stresses that the promise and potential benefits of ai in economic and societal terms appear to be tremendous; ### 78. states that it is important to examine and categorise potential risks to fundamental rights posed by ai , however the case studies illustrated that ai technologies also enable us to apply , effective countermeasures that are able to mitigate or eliminate the very same risks; underlines that , ai is still in its early stages of development within a wider context of emerging technologies; highlights the significant imbalances of market power present in data markets and the adjacent ai economy; stresses that fair competition and removal of obstacles for start-ups and sme to compete are essential to fairly distribute the potential benefits of ai in economic and societal terms;


### 79. observes fierce ai competition that involves not only the two frontrunners , the us and china , but also countries such as canada , india , israel , japan , russia , south korea and the uk; underlines that the previous chapter has already indicated that the eu is so far struggling to meet its aspiration30 of becoming a global leader in ai; ### deleted


### 80. examines in the following the eu’s global competitiveness with regard to ai by comparing it with that of china and the us , focusing on three core elements: regulatory approach , market situation and investments; ### 80. examines in the following the eu’s global competitiveness with regard to ai by comparing it with that of china and the us , focusing on three core elements: regulatory approach , market situation and investments; recognizes however that transnational markets and corporations cannot easily be delineated across national borders;


### 81. notes that the us refrains from introducing horizontal legislation in the digital field , while focusing instead on sector-specific laws and private sector innovation , in particular among its tech giants and leading universities; observes that the us approach on ai up to 2019 can therefore be summarised as providing legal guidance to businesses , investing in research projects and removing barriers to innovation; ### 81. notes that the us has not yet introduced horizontal legislation in the digital field , and has so far focussed on sector-specific laws and private sector innovation , in particular among its tech giants and leading universities;


### 82. stresses that the 2019 american ai initiative act ushered in a slight realignment , as besides redirecting funding , retraining workers and strengthening digital infrastructure , the us government announced the development of common standards for trustworthy ai; notes , however , that the resulting 10 principles were very broadly formulated in order to allow each government agency to create sector-specific regulations; expects that although the administration of president biden plans to bring forward a new bill of rights to limit ai harms in 2022 , the us approach will remain market-driven , aiming to avoid regulatory overreach; ### 82. stresses that the 2019 american ai initiative act ushered in a slight realignment , as besides redirecting funding , retraining workers and strengthening digital infrastructure , the us government announced the development of common standards for trustworthy ai; notes , however , that the resulting 10 principles were very broadly formulated in order to allow each government agency to create sector-specific regulations; notes that the administration of president biden plans to bring forward a new bill of rights to limit ai harms in 2022 ,


### 83. highlights that chinese president xi jinping underlined as early as 2013 the importance of technologies in geopolitics , the role of public policies in defining long-term objectives and the fact that ai offers an opportunity to overtake the us in terms of military supremacy; stresses further that the chinese government subsequently put forward the made in china 2025 plan in 2015 and the next generation ai development plan in 2017 , both of which had the clear targets of making china the global leader in ai by 2030; notes that the 2018 ai standardisation white paper further outlined how the socialist market economy can develop international standards and strategically engage in international standardisation organisations; ### 83. highlights that chinese president xi jinping underlined as early as 2013 the importance of technologies in geopolitics , the role of public policies in defining long-term objectives and an opportunity to overtake the us in terms of military supremacy by means of ai technologies; stresses further that the chinese government subsequently put forward the made in china 2025 plan in 2015 and the next generation ai development plan in 2017 , both of which had the clear targets of making china the global leader in ai by 2030; notes that the 2018 ai standardisation white paper further outlined how the socialist market economy can develop international standards and strategically engage in international standardisation organisations;


### 84. observes that on the global stage , china actively promotes international ai partnership as a way to export its own ai-based government surveillance practices , social scoring system and censorship strategies; emphasises that heavy investment abroad under the digital silk road initiative are also used as a means to spread chinese ai globally and to bring other countries under chinese influence; concludes that the chinese approach is therefore built upon deploying ai domestically as well as exporting ai technologies that follow predetermined standards that are in line with the ideology of the chinese communist party; ### 84. observes that on the global stage , china actively promotes international ai partnership as a way to export its own ai-based government surveillance practices , social scoring system and censorship strategies; emphasises that heavy investment abroad under the digital silk road initiative are also used as a means to spread chinese ai globally; concludes that the chinese government’s approach is therefore built upon deploying ai domestically as well as exporting ai technologies that follow predetermined standards that are in line with the ideology of the chinese communist party;


### 86. emphasises that central to the eu regulatory approach is a strong attention to ethical considerations in line with core human rights values and democratic principles; underlines that the commission thereby hopes to achieve another gdpr-like ‘brussels effect’ , meaning that the eu’s regulatory and market power leads to a competitive edge in ai; states that establishing the world’s first regulatory framework for ai could indeed leverage a first-mover advantage in setting international ai standards based on european values , as well as successfully exporting ‘trustworthy ai’ around the world; ### 86. emphasises that central to the eu regulatory approach is a strong attention to ethical considerations in line with core human rights values and democratic principles; states that establishing the world’s first regulatory framework for ai could indeed leverage a first-mover advantage in setting international ai standards based on european values , as well as successfully exporting ‘trustworthy ai’ around the world;


### 87. is aware that that the vast majority of the 100 leading ai companies globally are domiciled in the us , whereas only three chinese companies and four eu companies fall into this category34 ; notes that the us also leads in the total number of ai start-ups , hosting 40 % of all new ai companies , followed by the eu with 22 % and china with 11 %35 ; ### deleted


### 88. points out that in recent years , many of the eu’s most successful digital companies have been acquired by us tech giants; refers also to the ongoing debate about so-called ‘killer acquisitions’; notes that us firms , with 130 acquisitions in 2020 alone , acquired many more ai companies than eu and chinese firms combined , which made 30 and three comparable acquisitions respectively; ### 88. points out that in recent years , many of the eu’s digital companies have been acquired by us tech giants; refers also to the ongoing debate about so-called ‘killer acquisitions’; notes that us firms , with 130 acquisitions in 2020 alone , acquired many more ai companies than eu and chinese firms combined , which made 30 and three comparable acquisitions respectively;


### 89. stresses that while the us and china are trying to accelerate the use of ai technologies in the public and private sectors , the adoption of ai within the eu lags behind; states that only 7 % of all eu companies are currently using ai technologies , while just 30 % are planning to do so in the future36 ; states that there is also a clear gap in ai readiness between different business sectors as well as among member states , with southern and eastern europe lagging behind , while northern europe is , in general , very advanced , even by global standards; ### 89. stresses that while the us and china are trying to accelerate the use of ai technologies in the public and private sectors , the adoption of ai within the eu lags behind; states that only 7 % of all eu companies are currently using ai technologies , while just 30 % are planning to do so in the future36 ; states that there is also a clear gap in ai readiness between different business sectors as well as among member states;


### 90. underlines that while the us and china each have a unified digital market with a coherent set of rules , the eu’s digital single market is still fragmented and features many barriers; stresses that the development of ai products and services is further slowed down by the existence of 27 different national ai strategies and the fact that the eu’s ai assets such as talent , capital and research are spread widely across the continent; ### 90. underlines that while the us and china each have a unified digital market with a coherent set of rules , the eu’s digital single market is still fragmented and features many barriers;


### 91. points also to the problem that inconsistencies in eu law , contradictions between eu and national laws , different legal interpretations and a lack of enforcement among member states is putting european companies in operational and financial jeopardy as they cannot determine whether their ai innovations are later likely to be assessed as non-compliant with eu law; ### 91. points also to the problem that inconsistencies in eu law , contradictions between eu and national laws , different legal interpretations and a lack of enforcement among member states risk creating uncertainty for european companies as they may find it difficult to determine whether their ai innovations are later likely to be assessed as non-compliant with eu law;


### 92. notes that the insufficient legal certainty for ai companies is further exacerbated by the fact that common standards and norms are missing in some sectors , while others are compromised by overregulation or the presence of legislative proposals that have been pending for long periods of time without being adopted; highlights as an example the fact that eu ai developers face a data challenge that neither their us nor chinese counterparts do; observes that they often do not have enough high-quality data to train their algorithms , struggle with strict data protection rules and are affected by a lack of sectoral data spaces and cross-sectoral interoperability , as well as constraints on cross-border data flows; ### 92. notes that the insufficient legal certainty for ai companies is further exacerbated by the fact that common standards and norms are missing in some sector; highlights as an example the fact that eu ai developers face a data challenge that neither their us nor chinese counterparts do; observes that they often do not have enough high-quality data to train their algorithms and are affected by a lack of cross-sectoral interoperability , as well as constraints on cross-border data flows;


### 93. points out that although private investments in the eu ai industry are rising strongly , with eur 3.4 billion invested in 2018 , the investment gap compared with the us (eur 31 billion) and china (eur 21 billion) has grown further37 ; states that the us is also leading in venture capital and private equity funding , which is particularly important for ai start-ups , with eur 12.3 billion , against eur 4.8 billion for china and eur 1.2 billion for the eu; notes that as a consequence , many european ai entrepreneurs are crossing the atlantic to scale up their businesses in the us; ### 93. points out that private investments in the eu ai industry are rising strongly , as are venture capital and private equity funding , which is particularly important for ai start-ups; stresses nonetheless that the liquidity of eu financing markets for technology companies lacks the scale of comparable markets in the us; notes that as a consequence , many european ai entrepreneurs are crossing the atlantic to scale up their businesses in the us;


### 94. states that together with national initiatives , the estimated annual public investment of the eu in ai of eur 1 billion is also much lower than the eur 5.1 billion invested annually in the us and up to eur 6.8 billion in china38 ; states , however , that between 2017 and 2020 , eu public funding for ai research and innovation increased by 70 % compared to the previous period; acknowledges that the commission plans to increase investment further through the digital europe programme , horizon europe , the european structural and investment funds (esif) , the european investment fund (eif) , the recovery and resilience facility and various cohesion policy programmes39 ; ### 94. states that between 2017 and 2020 , eu public funding for ai research and innovation increased by 70 % compared to the previous period; acknowledges that the commission plans to increase investment further through the digital europe programme , horizon europe , the european structural and investment funds (esif) , the european investment fund (eif) , the recovery and resilience facility and various cohesion policy programmes39;


### 95. stresses that ai companies within the eu have problems finding qualified employees as 42 % of the eu population lacks basic digital skills40 ; points out that the eu also struggles with ai-relevant university degrees , as the number of bachelor’s degrees awarded in ict is decreasing while the number of postgraduate degrees awarded is 50 % lower than in the us; underlines that the eu also faces a cybersecurity skills gap , with more than 350.000 experts needed; recognises that us and increasingly also chinese companies have a clear advantage in attracting and retaining ai talent from the rest of the world; ### 95. stresses that ai companies within the eu , like many other companies , face strong competition for qualified employees; points out the need to form and attract a substantially higher number of well-educated graduates to work in the digital sector;


### 96. observes that although the eu still has an excellent community of researchers who are producing many ai research papers that are often cited and downloaded , the eu’s global impact is steadily declining , with a brain drain of top eu researchers to the us and china41 ; notes that the eu only spends 2 % of its gdp on research and development (r&d) , while the us spends 2.8 %; emphasises that the total r&d spending of us software and computer services firms in 2019 was eur 100 billion , which is much higher than comparative figures in china and the eu , where private r&d spending amounted to eur 20 billion and eur 12.5 billion respectively; ### 96. observes that although the eu still has an excellent community of researchers who are producing many ai research papers that are often cited and downloaded , the eu’s global impact is steadily declining;


### 97. notes that the eu’s digital infrastructure is underdeveloped , with just 25 % of people in the eu being able to connect to a 5g network , compared to 76 % of people in the us42 ; observes that the eu in general lacks high-performance digital infrastructure with interoperable data spaces , affordable energy supply , high transmission rates and volumes , reliability and short delays as well as a genuine ai ecosystem with excellence clusters such as can be found in the us or china43 ; ### 97. notes that the eu’s digital infrastructure is underdeveloped , with just 25 % of people in the eu being able to connect to a 5g network , compared to 76 % of people in the us42 ; observes that the eu lacks sufficient high-performance digital infrastructure with interoperable data spaces , high transmission rates and volumes , reliability and short delays;


### 98. concludes that the us is still the overall leader in ai as it is ahead in almost every category , in particular when it comes to market power , investment , ai talent , research and infrastructure; highlights , however , that china , which five years ago was still significantly lagging behind the us in all indicators , is now quickly catching up in almost every category; notes that china could in fact achieve its goal of becoming the global leader in ai by 2030 or even earlier; recognises that both countries have the advantage of a unified single market , greater flexibility in digital governance and stronger political commitment to remaining a leader in ai; ### 98. concludes that the us is still the overall leader in ai as it is ahead in almost every category , in particular when it comes to market power , investment , ai talent , research and infrastructure; recognises that both countries have the advantage of a unified single market;


### 99. stresses that the eu is behind the us and china in virtually every category and that despite its current measures , it is losing further ground; notes that the eu is , however , ahead on regulatory approaches; points out that a viable eu strategy for becoming more competitive on ai would be to quadruple efforts to catch up when it comes to ai research and innovation , skills , infrastructure and investment , while at the same time trying to create a first-mover advantage by establishing a future-oriented and innovation-friendly regulatory framework for ai development and use; ### 99. notes that the eu is ahead on regulatory approaches; points out that an eu strategy for developing its ai sector would be to combine investments in innovative solutions , skills and infrastructure with a focus on fair competition and a future-oriented and regulatory framework for ai development and use;