The future has arrived -what do we want the future to be?
Science and technology create new options and opportunities for society. The printing press democratized knowledge, the industrial revolution replaced manual labor with machines, and the internet connected everyone. Each advanced society, each also disrupted society in the advance. This new age of autonomous systems portends replacing human intelligence with machine intelligence everywhere human intelligence and judgment are used. It, too, can be expected to profoundly advance and disrupt society. However, the path there is not free and not straightforward. It will be a whole-of-society endeavor to tame and shape our autonomous systems and assure that technology is safe, secure, fair, equitable, dependable, and accountable. How autonomous systems will be managed is at the crossing of science, technology, society, policy, and governance.
Society will respond, as societies have, through their institutions of government, professions, industries, education, and leaders.
Throughout history it has never been a smooth process to balance the public interests, acceptable risks, norms, national security, and industry's profit motive. Public-private partnerships that nurture innovation can try the patience of governments, their stakeholders, and the public when the supply chain is too slow to respond and when risks turn into liabilities.
Autonomous systems will continue to face the same societal pressures as technology revolutions of the past have faced.
However, these systems are different from earlier technologies. Replacing humans and human intelligence with machines and machine intelligence is replacing within existing frameworks of laws, ethics, morality, norms, as well as an existing technology base. A software flaw in an autonomous system, for example, is no longer just a bug; rather, it is a potential violation of law and morality.
Assured autonomy, the subject of this workshop series, is central to the discussion of the future of autonomous systems and the revolution they will surely bring. Lack of assurance manifests in longer development time, at more cost, more patching post deliverable, the acceptance of new and often unjustified risks from new sources of deployed autonomous systems, unavailability and unaffordability of insurance, and the lost opportunity costs from an important technology not existing or not available when needed most. The current pandemic offers examples of lost opportunities. With irony, the perceived benefits of autonomy, if attempted without the necessary assurance, may be negated by the lack of the assurance. Assured autonomy is about understanding and mitigating risks of operating autonomous systems in our society.
The science underpinning assurance is itself still an active interdisciplinary research area, as this workshop has shown, with critical implications on the future of autonomous systems and the society in general. Too strong a requirement for assurance with the enforcement mechanisms of government may stifle innovation while keeping the public safe today but exposing society to future shocks. Too weak a requirement for assurance places the public at risk today with little protection from future shocks.
The challenge of establishing assurance in autonomy is rapidly attracting increasing interest in the industry, government, and academia. Autonomy is a broad and expansive capability that enables systems to behave without direct control by a human operator. To that end, it is expected to be present in a wide variety of systems and applications. A vast range of industrial sectors, including (but by no means limited to) defense, mobility, health care, manufacturing, and civilian infrastructure, are embracing the opportunities in autonomy yet face the similar barriers toward establishing the necessary level of assurance sooner or later.
Numerous government agencies are poised to tackle the challenges in assured autonomy.
It is important to note that how we analyze, address, measure, and deploy autonomous systems is embedded in an existing set of laws, norms, policies, and societal values, as well as in disciplines. Controls, computer science, machine learning, artificial intelligence, human factors, communication, perceptual and cognitive sciences, business, law, ethics, and public policy are only a few among the disciplines that address the underlying scientific problems in assured autonomy.
Given the already immense interest and investment in autonomy, a series of workshops on Assured Autonomy was convened to facilitate dialogs and increase awareness among the stakeholders in the academia, industry, and government. This series of three workshops aimed to help create a unified understanding of the goals for assured autonomy, the research trends and needs, and a strategy that will facilitate sustained progress in autonomy.
The first workshop, held in October 2019, focused on current and anticipated challenges and problems in assuring autonomous systems within and across applications and sectors. The second workshop held in February 2020, focused on existing capabilities, current research, and research trends that could address the challenges and problems identified in workshop 1. The third event was dedicated to a discussion of a draft of the major findings from the previous two workshops and the recommendations.
The next two sections of the report outline the findings and recommendations. We intend these findings and recommendations to be usable by the government agencies for building and refining research and development programs and the science and by technology policymakers for setting priorities. The following two sections provide more details on the opportunities and challenges in several disciplines and applications.
The major advances in the technology for autonomy over the past decade or so promise a transformation driven by autonomy over the coming decades. We are at the beginning of the transformation. The findings of this workshop series outline the challenges and shortcomings that need to be addressed so that autonomy can sustainably fuel a long-lasting transformation.
The findings embrace the (already vast and likely underestimated) extent and heterogeneity of the challenges to be overcome.
They also advocate for coordination among all disciplines -technical and "non-technical" -and all stakeholders.
◗ No good can come from autonomy without proper assurance.
◗ The goal of assured autonomy is human-centered -to amplify, augment, enhance, and empower humans.
◗ Autonomous systems have a diverse set of vulnerabilities.
◗ Open operation environments amplify technical challenges.
◗ Assurance is context-dependent and not once-and-for-all.
◗ Assurance for autonomous systems requires a major re-think.
◗ Means for measuring the progress and characterizing the gaps are necessary.
◗ Autonomy is a socio-economic opportunity and challenge.
◗ The public will perceive and be affected by autonomy unevenly.
◗ The challenges in autonomy require interdisciplinary approaches.
◗ Strengthening connections between the stakeholders will accelerate progress.
◗ A re-envisioning of education and workforce development must be part of the path forward.
◗ A national research strategy for assurance is needed.
Assurance, in this context of autonomy, cannot be an afterthought, and "assured" is not just one of the qualifications for autonomy. Autonomy can survive as a useful technology and fuel the envisioned transformation only with proper assurances.
The disciplined principles and practices-yet to be developed-for assurance will create the medium in which the interdisciplinary approach this report advocates can be realized. Finally, while the emphasis should be on generalizable principles and practices, assurance will look different for different stakeholders, as each application will carry different risks and trade-offs.
The recommendations aim to outline the high-level characteristics of an approach toward addressing the technical, economical, and societal challenges for the adoption of autonomy at scale and the creation of a long-term transformation fueled by autonomy.
They also identify the potential roles for different stakeholders in the implementation of this approach.
We emphasize that this report intends to be an invitation -not a final statement -for a comprehensive effort that is interdisciplinary beyond all past efforts on autonomy.
The following will distill several major findings from the workshop series. These findings point to challenges and opportunities (at times simultaneously) in autonomy.
They include overarching properties that appear across disciplines and the need for change in how we do research, educate students, and/or operate autonomous systems.
Our ability to establish appropriate assurance for autonomous systems will determine how effectively we can manage their risks and benefits. Lack of appropriate assurance may drive the cost of development uneconomically high, delay adoption of autonomous systems at scale, or result in catastrophic consequences.
Lack of assurance can potentially negate the perceived benefits of autonomy.
The goal of assured autonomy is to develop systems that are not only trustworthy, but that will also amplify, augment, enhance, and empower humans. Such humancentered autonomy can be achieved only by accounting for human-autonomy integration and human values throughout the design and lifetime of autonomous systems and developing (human) participatory approaches for the specification, assessment, and governance of these systems. 
Autonomous systems are to operate in environments that cannot be confined within clear boundaries. By the very nature of autonomy, these environments will evolve over time, may change abruptly, drastically, and unexpectedly, and may include adversaries. These characteristics amplify the challenges in every element of assurance, from actionable specifications of behavior to verification to certification.
An assurance argument is useful only in its intended context, i.e., the environment characteristics, the assumptions in reasoning, the fault and threat models, etc. Therefore, it is critical to explicitly and unambiguously express what the outcome of assurance does and does not guarantee and how robust or sensitive it is to shifts in the context. Additionally, it is desirable for an assurance argument to continually adapt to the changes in the context in a manner transparent to all stakeholders.
Autonomous systems feature a level of complexity that is unprecedented in systems for which there exist principles and practices for systematically establishing assurance.
This demands a major re-think of assurance -rather than trying to merely re-purpose the existing practices.
The breadth and interdependencies of the challenges complicate measuring the progress and raise a need for new benchmarks and testbeds. Furthermore, a catalog of the existing capabilities and acceptable reference systems may help prioritize for more pressing needs and adjust the performance expectations.
Autonomy will potentially affect virtually all aspects of society. Some effects are immediate, such as removing the driver from vehicles; some effects are indirect and realized over time. For example, the implications of driverless vehicles can be traced all the way to the new business opportunities, the need for new employment opportunities, the changes in the future of work, urban planning, law, governance, and policy, and the implications for the environment. A holistic perspective that embraces all socio-economic aspects -coupled with the technical aspects -of autonomy is necessary for proper incentives for assurance and adoption of autonomy at scale.
What is an acceptable level of assurance for autonomous systems will evolve over time as people begin to understand the benefits and the risks. The impact of autonomy on different stakeholders and, even more importantly, on different demographic groups of the society will be different.
The challenges in autonomy are interdependent. For example, such interdependencies induce correlations and trade-offs between the technical requirements and measures for safety, security, and privacy -three of the properties autonomous systems are expected to respect.
Deferring the progress for any of them may undermine the progress for the others. Similarly, coordination toward addressing the technical, regulatory, legal, and ethical challenges may create not only an impact greater than otherwise possible but also cross-fertilization.
Building stronger connections between the stakeholdersacademia, the industry, the regulators, the policymakers, and the public -would improve efficient use of the resources and better align objectives and approaches.
An important -if not the most important -need for affordably developing and deploying autonomous systems is an appropriately trained workforce. Arguably no academic program offers the necessary interdisciplinary approach, and recent studies point to a growing mismatch between how academia perceives the gaps in education and training and how industry and government do.
Assured autonomy is a national challenge. Yet there currently exists no coordinated approach, no organized community response, and no national research program searching for answers.
We stand at the beginning of the next wave of technological change that assured autonomy will bring.
It will fundamentally alter our relationships to our critical infrastructures, our national defense, our lands, our lives, and livelihoods. In these different contexts assured autonomy must be viewed. Each context has its own risks and priorities, its own knowns and unknowns, and its expectations of consequences. Research and development for assured autonomy in the complex fabric of society is inherently interdisciplinary, convergent, crosscutting, and broad. The recommendations presented here offer a vision for exploration, innovation, and advancement that will make our society more prepared for this new technological transformation.
The recommendations build a vision for progress around a "network of institutes on autonomy" and briefly describe possible roles for the researchers, educators, 
FORWARD.
1 https://www.state.gov/declaration-of-the-united-states-of-america-and-the-united-kingdom-of-great-britain-and-northern-ireland-on-cooperation-in-artificialintelligence-research-and-development-a-shared-vision-for-driving/ research sponsors, and the organizations sponsoring this workshop series.
◗ embracing the interdependency between the challenges; and ◗ developing a holistic view of these interdependent challenges.
The education and workforce development community can contribute by...
•◗ aligning its priorities with the needs of the industry and government;
◗ focusing on establishing a foundation that will enable the students to acquire interdisciplinary skills; and ◗ broadening and diversifying the basis for qualified workforce in autonomy.
The research sponsors can contribute by... 
This section details the challenges, trends, and needs that were brought up during the workshop panels and breakout discussions and in the focused virtual panels conducted after the workshops. While this overview breaks the presentation into several categories, there is a tight coupling among the challenges, trends, and needs in these categories.
Over the last several decades, the field of formal methods for verification and testing has amassed a rich body of provide guidance to reduce the necessary testing effort;
and help eliminate unintended functionality; and identify the gaps in the requirements.
Perhaps the most remarkable difference between conventional safety-critical systems and autonomous systems is that, in the latter, learning and adaptation play a central role. That is, the knowledge about the system and the environment that is assumed in formal verification changes at run time, and static, design-time verification is likely to be insufficient. The approach must fundamentally shift from "design, verify, and deploy" to "design, provisionally verify, continually re-validate the assumptions, and continually iterate the design."
Therefore, the community should invest in methods that are adaptive and tightly coupled to learning and verification and receptive to the computational, power, and thermal limitations at run time.
A secure system is one that will behave as designed and implemented even when under attack. It follows that security is a necessary condition for assured autonomy.
Each The first of the problems comes from the reliance of the machine-learning-based frameworks on large image processing libraries; several researchers discovered cyber vulnerabilities in these libraries. Attackers can use these vulnerabilities to modify incoming sensor data as well as to divert the control flow of the system to attacker-provided code. The second issue arises from the dependence on large training data sets. Attackers have found at least two ways to corrupt systems that rely on these large data sets: "poisoning" the training data to cause the system to be incorrectly trained; and using "adversarial examples" or images designed to fool the classifier.
Until these cyber-security issues are resolved, there cannot be much faith that an autonomous system will behave as intended. Sadly, the advantage is still with the attacker in an "arms race" in which defenders discover a vulnerability and patch it while the attacker modifies existing attacks in ways that bypass the defense. Attackers seem to be able to innovate faster than defenders, allowing them to maintain an advantage. There is an ongoing supply chain. Suppliers from many locations increase the attack surface by orders of magnitude. Attackers merely find one vulnerability to exploit while the defender must defend against many vulnerabilities. integration of learning-enabled elements), the potential exposure to non-cooperative actors, and emerging privacy concerns. Certification for autonomy will require a major re-thinking as even how one can establish specifications against which the systems should be certified-let alone the certification process itself-is currently unclear and underexplored. Even assured autonomy would not remove the need for human operators to be licensed in some way.
The regulatory and legal system will need to address issues of responsibility and liability with regard to autonomy. Even in systems overseen by human operators, autonomy may already be the cause of faults, for example by providing erroneous, insufficient, or late feedback on problems occurring in the system. The questions that need to be addressed include the following:
◗ Who is responsible when a fully autonomous system causes a loss event?
◗ Who is responsible when an autonomous system interacting with humans causes a loss event when it is not evident whether fault lies with the human, the autonomous system, or a mix of both?
◗ If liability waivers are made available to manufacturers and/or operators, will that reduce pressure to create safe vehicles, resulting in "gaming" of safety standard compliance?
The potential reasons to regulate autonomous systems 
The goal of the future progress in autonomy should be to have human-centered interdependence autonomy that is not only reliable, safe, responsible, and trustworthy, but that will also amplify, augment, enhance, and empower people. In fact, the best human-autonomy team should be able to outperform the best human or the best machine. Alternatively, if the autonomy does not behave as humans would expect, or if it is unreliable, or if it lacks transparency, then humans will not use it, will mistrust it, or will over-trust it. The literature and recent incidents witness the potentially catastrophic consequences of disconnect from humans.
Human-centered autonomy can be achieved only by considering human-system integration needs throughout the system's lifetime. Sample questions that guide various stages of the lifetime include the following:
◗ In the specification stage, how do we translate our goals for human society into performance specifications for autonomous systems?
◗ How is contextual knowledge transferred from humans to the machine during the design process?
◗ What test and evaluation paradigms are needed to understand the contexts in which systems will perform safely and effectively?
◗ In system operation, how do we translate contextual knowledge of system capabilities to human operators (particularly when they may not be versed in the underlying technology)?
◗ During system evolution, when do system updates require recertification of the system, the human operators, or their combination, and when do they need human operators to be retrained?
The recent trends toward human-autonomy teaming offer a promising medium to investigate human-system integration needs. As Johnson and Vera 3 articulated, "No autonomy is an island." Therefore, assurance of autonomy 
Although there is no universally accepted definition, privacy may be interpreted as the right of the individuals to control where their data are used. Technology typically makes it easier to collect data, monitor people, and make inferences about people. Autonomous systems raise new concerns or amplify the existing concerns because data collection is increasingly more pervasive, adaptive, responsive, and because autonomous systems directly interact with humans in the physical world.
Collecting data is a privacy concern in itself because, regardless of the purpose for collecting the data, others may access the data. Thus, the mere storage of it can create privacy concerns later. Additionally, the inferences that can be drawn from the data may be outside the imagination of the technology developers and users, regulators, lawmakers, and the public. Furthermore, the lack of algorithmic maturity and the mismatch between the ability to collect data and the ability to turn the data into task-oriented knowledge could result in the collection of unnecessary data.
Data collection by autonomous systems tends to be invisible and transparent. For example, monitoring by police is an activity more noticeable by humans compared to aerial surveillance. Additionally, autonomous systems have the potential to shape people's environments so that people believe they are acting in their self-interest. Yet, the system is actually intruding on their decision autonomy.
The meaning of privacy depends on social interactions and human values. It is, therefore, necessary to develop the technical means to facilitate an informed public debate, while being aware of the risks of identifying the standards and guidelines for the assurance of privacy in the development and operation of autonomous systems.
These standards and guidelines should embrace the diversity among the individuals of the society and not focus on merely a hypothetical "average" individual.
Privacy is not a standalone concern; it introduces trade-offs with the other aspects of assured autonomy discussed in this report. 
Forecasting the societal impacts of autonomy is no easy task. Autonomy is a behavior, and as such, it can be present in a wide variety of platforms and in ways we cannot currently predict. Determining the causality and compounding relations between the societal effects is a challenge that can be addressed only through the comprehensive and interdisciplinary approach advocated in this report. Yet these determinations is the precursor for influencing the evolution of autonomy toward a net-positive impact. The student body associated with the specialties relevant for autonomy is exceptionally diverse, both in maturity and in prior expertise, and will further diversify as the field moves from proofs-of-concept for the technology to integration at scale and creation of positive societal impact. Therefore, establishing a foundation to enable the students to acquire interdisciplinary skills will be essential.
Finally, a societal transformation fueled by autonomy and the necessary transformation in education can be achieved only by expanding and diversifying the basis for the workforce that will sustain the growth in the research, development, deployment, operation, and governance of autonomous systems. To this end, it is critically important to create a culture of inclusion in education for autonomy and in the field of autonomy through education. Assurance is essential for society to understand what risks it is taking with the increased adoption of autonomy.
The weaknesses in our understanding of assurance for autonomy manifests itself in policy. Often it is not until a harm cannot be ignored that a policy is patched. A strategic societal and policy approach to this technology revolution is preferable to a patchwork of exceptions to redress a real (or imagined) harm.
Understanding acceptable risks and risk mitigation are critical steps to making good policy for autonomous systems and for deciding what future with autonomy we want and how to get there. With the self-driving car prototypes on the roads today, society, and particularly policymakers and technologists, must decide on the paths going forward and with what acceptable assurances. The strategies adopted and investments made will determine how well and how deeply autonomous systems become part of society.
The governing bodies, interpreted broadly beyond the federal government, have the responsibility to represent the public interest by regulating its risk appetite and correcting for the failures and timelines of the market (e.g., attempts to deploy autonomous systems without proper assurance). In principle, these governing bodies are supposed to predict the concerns related to security, privacy, fairness, accountability, and liability and take steps for preempting, mitigating, or compensating for the socially unacceptable outcomes of these concerns.
Additionally, ensuring access to resources, information, and opportunities to participate in the development and operation of autonomous systems hinges on their decisions.
Benefit redistribution throughout the envisioned longterm transformation fueled by autonomous systems is also among their responsibilities. While this redistribution may seem to be an issue of the future. the impact of autonomy on the society will potentially be systemic, and failing to account for benefit redistribution after largescale adoption of autonomous systems will only worsen the benefit imbalance.
The roles for the governing bodies include: establishing mechanisms and boundaries to prevent an unjustified shift of risks (e.g., from the commercial sector to the public) and liability (e.g., from the developers to the operators to the insurance sector or the reverse); providing third-party oversight of development and deployment; and conducting third-party review of the autonomous system market. In If and when autonomous systems operate with assurance at near-peer levels of human performance and beyond, the relationships among people, corporations, and autonomous systems will evolve and be codified into accepted practice, profoundly affecting the future of work and living in society.
Governing principles for autonomous systems (and the neighboring field of artificial intelligence) are emerging 14 15 16 along with the disparities in the approaches, e.g., between the US and Europe on the level of restrictions. Dictating the right approach for governance and policymaking for autonomous systems is beyond the scope of this report.
However, we emphasize that there is no shortcut for appropriate assurance in autonomy, and the choices that seem cost-saving today may result in higher costs in the future.
This section presents a collection of use cases of autonomous systems. While this exposition is highly biased toward the interests of a subset of the workshop participants and is nowhere close to offering a fair coverage of the potential uses of autonomy, it is intended to provide a concrete interpretation of the challenges, trends, and needs (discussed in the previous section at a general and abstract level) in the context of a few applications. The extent of the potential uses for autonomy will be limited only by our imagination and our ability to establish appropriate assurance for what we can imagine.
Urban air mobility (UAM) has the potential to revolutionize mobility around densely populated metropolitan areas by providing a practical air transportation system for passengers 
Autonomous vehicles have the potential to improve the safety, efficiency, and sustainability of the transportation system. They would provide new mobility options, especially for those with some form of disability.
Autonomous vehicles would take human driver error out of the equation and allow the time and money spent commuting to be put to better use (possibly at the expense of increased vulnerability to other sources of human error, e.g., in design, validation, and maintenance).
Assured autonomy plays a vital role in the performance of autonomous vehicles. In particular, the United States Code for Motor Vehicle Safety (Title 49, Chapter 301) defines motor vehicle safety as "the performance of a motor vehicle or motor vehicle equipment in a way that protects the public against unreasonable risk of accidents occurring because of the design, construction, or performance of a motor vehicle, and against unreasonable risk of death or injury in an accident." Additionally, the assurance of autonomous vehicles potentially may shape the public attitude toward the adoption of the technology, which is essential for its benefits to be realized.
As it turns out, the development timeline of autonomous vehicles is much longer than many early predictions. 21
The uncertain and unstructured nature of environments in which the vehicles operate highly contributes to this delay as it adds complexity to the technical challenges discussed in Section 4. First, the open-world challenge leads to an unreasonably large number of test scenarios and drives the question of edge cases from the safety and verification as well as the certification and regulation aspects. Furthermore, the lack of system specifications and requirements is a significant impediment toward formal methods realizing their full potential in this domain.
Such requirements need to be precise and not to rely on assumptions regarding human nature or capabilities, In their early adoption, autonomous vehicles will likely share the road with human-driven vehicles. Additionally, they may need to hand over the control of the vehicle to the drivers under certain situations, especially those with less than level-5 autonomy. The challenge of humansystem integration and trust needs to be addressed to ensure that the introduction of autonomous vehicles does not cause more accidents in the overall transportation system due to factors such as their conservative behaviors, inconsistencies with local driving practices, or confusion over the control of the vehicle. Security is also essential and is not an after-the-fact question since malicious attacks on sensor measurements or the vehicle's network may lead to fatal consequences. The current regulatory regime in the US is "self-certification,"
with regulatory oversight of potential computer-based system faults done primarily in reaction to on-road incidents and loss events. There is no consensus on how "driver licenses" will be handled for autonomous vehicles.
The US DOT currently has a "non-regulatory" policy toward automated vehicle technology. 22
Autonomous robots have the potential to address urgent environmental problems --cleaning the oceans, recycling waste, and managing forests, for example. Their impact presents a complex composition of social, economic, and political challenges. For example, the recycling industry generates over $117B in economic activity in the US alone and employs more than half a million workers. Steady increases in domestic waste production, inefficiencies in current waste-handling practices, and changing international regulations have created an urgent need for innovative waste management strategies. While the industry heavily depends on human workers and manual work, recruiting and retaining workers for these stressful, hazardous, and poorly paid jobs have become challenging. Any shift in the industry hinges on reaching consensus among a large set of stakeholders-including consumers, packaging suppliers, recycling companies, workers, technology providers, and regulatory bodies -all with potentially conflicting interests.
While autonomous robots offer obvious opportunities in the waste management and recycling industry, their adoption for narrowly defined technical objectives, despite their shortterm benefits, may result in unintended consequences.
Therefore, an interdisciplinary, holistic approach is necessary.
This approach must consider the implications of autonomous robots for the future of recycling work and workers and how such technology may change the characteristics and meaning of the job. For example, robots may reduce the frequency of injuries to waste-sorters., which currently is twice the industry average. Nevertheless, robots are currently capable of performing only those tasks that are also easiest and safest for human. Thus, the goal of realizing the most immediate efficiency and profitability gains from robotics conflicts with improving worker safety. A promising strategy for creating the intended levels of impact may be through properly designed human-autonomy co-work.
The COVID-19 pandemic has highlighted the importance of the waste management work as an essential service. It also has amplified the vulnerabilities of the recycling market. The crisis has impeded the international waste and materials trade, causing significant disruptions in the market. It has introduced additional risks for worker safety and new concerns for retaining workers and assuring work continuity. Therefore, an expedited adoption of autonomous robots may be expected in the near future. Such an adoption will increase the importance of assurance in the deployment and operation of autonomous robots and of a number of challenges discussed in the preceding sections, including safety, human-autonomy interactions, regulation, and possibly even ethics and privacy.