Understanding Responsible AI and its Implementation In Present-Day Corporate Landscape
Tags: ai, aiinbusiness
Published: 2022-09-05 10:11:43

Artificial intelligence will shape a large part of the corporate landscape as 9 out of 10 leading businesses have ongoing investments in it. 97% of the corporations are investing in data initiatives, while 91% are investing in AI activities.

At the same time, increased usage of AI imposes many threats that the corporate world is yet to acknowledge completely. While companies are working to reduce cybersecurity-related risks of AI, many other threats, like biased data labeling, largely remain out of focus. Amidst such potential chaos, corporations must understand their role and responsibilities when using AI/Ml technologies in any capacity.  

## Major Advantages & Challenges Businesses Commonly Face with AI/ML

Artificial Intelligence has added an edge to information technology. The advantages of AI in IT management and decision-making have encouraged a lot of companies to use it, while many others are making efforts to understand its usability in their niche.

The challenge is not limited to finding the feasibility of a project built using AI but also in creating an AI solution that addresses people, products, processes, and privacy concerns all at once. An AI solution that misses on any of these may fail one way or the other, resulting in loss to the business.

A successful AI system cannot work alone, it will often require human interactions. The coordination between humans and AI systems is crucial in achieving respective objectives.

The major challenge for corporations is to choose where and how much humans can interact/interfere with AI and the boundaries under which AI will operate. Apart from this, how this system can be incorporated to achieve business operational goals is another key question companies need to answer. 

Designing an exact AI system process that establishes a balance between man-machine coordination is the only solution for companies. The following examples will help you understand successful AI and its limitations.

## Successful Usage of AI
The following can be considered successful AI systems that have transformed the corporate landscape:

### AI Robots
Artificial intelligence has been considered useful in the usage of robots. Specific use robots have evolved based on detailed data annotation, enabling them to solve problems and perform limited computation.

From iRobots used for vacuum cleaning, Hanson Robotics’s Sophia - a robot with human-like emotion and expression, to NAO from Softbank Robotics - used for education and research; There are many successful AI robot examples.

### Self-driving cars
One of the most talked about innovations is AI in autonomous vehicles. Though a lot of research is underway to make the technology more accessible with perfect data labeling as per the required situation, traffic rules, and local laws.
Autonomous vehicles use different sensors to collect data from surroundings. Data annotation helps the AI system to adjust accordingly and plan a smooth drive.
Some of the AI-based autonomous vehicles are - Cruise (San Francisco), Motional (Boston), Google's self-driving car project - Waymo (California), Tesla (Texas), etc.

### Healthcare AI
Machine learning and data annotation have brought in transformational development in healthcare diagnosis and treatment. AI has also been helpful in reducing the burden on healthcare facilities by reducing hospital visits, the workload on nursing staff/doctors, and pharma research.
For instance, Path AI based in Boston uses machine learning algorithms to analyze tissue samples. 

### AI in Transportation
Transportation is an industry that has gained the most from AI innovations. Real-time AI solutions based on a quick response from machine learning data sets can predict the most suitable route to destination with ease.

### Social Media AI
Social networks deal with huge amounts of data, AI has helped analyze these platforms and assist in providing a personalized and worthwhile user experience.
Massive amounts of data from social media platforms, including images, profile information, and preferences, is a tough task for humans. However, perfect data labeling backed machine learning algorithms enable AI to quickly perform this action. AI has also helped in the scrutiny of social media accounts to prevent rumor-mongering.
Meta’s Facebook, Slack, and Twitter are using AI for advanced management of platforms.

## Limitations of AI
There are certain restrictions to how AI can be utilized by companies, though the technology is under continuous development and research, its constraints need to be discussed to press for its better implementation. The following can be the limitations of AI:

### Sourcing Data
Gathering authentic data or sourcing it for any required AI system is a challenging task. Data has become one of the most sought-after commodities. In such a scenario, if the companies fail to access data, they won’t be able to create effective machine learning algorithms resulting in failed AI systems.

### Biased Data
If an AI system is fed with partial or bigotry information, it will cause a major operation flaw. Data annotation and subsequent AI operations are examined for perfection, but if the data fed into a machine learning algorithm is itself tainted, the result could be disastrous.
Currently, there is no particular system to check the entry of prejudiced data sets, which is a big threat to AI’s usage in companies. 

### Hardware Constraints
Though big companies are capable of developing sophisticated infrastructure and hardware to reduce the computing time for AI applications, small start-ups cannot do so. RAM and GPU cycles generally involve much computing time and result in firms being reluctant to step into the AI usage sector.

### Cost Constraints
Crucial processes like data mining, data annotation, and storage are costly. Companies need to invest in hardware and human resources. It involves high costs to train an AI model on the lines of the human brain, this again limits the development of AI.

### Safety and Privacy Concerns
The large amount of data processing that forms the backbone of AI systems and its dependency on this data alone for operation is the fact mostly raised by AI critics to question its reliability. Privacy is a major issue, as data leaks and subsequent threats cannot be ignored.
Another concern is disturbing the data labeling or inputting biased data into AI systems. 

## Biased Data in AI Models- A Major Corporate Challenge  
The prejudice in the operation of AI systems is deemed to have a widespread impact not only on the corporate landscape but on civilization as a whole. Though some may argue that a large part of these risks are mere assumptions, the real threat lies in the loopholes that emerge as a result of the technical gaps in the AI system’s operation.

AI bias is mainly related to the bias generated in a dataset during data mining, data labeling, or analysis. It gets affected by the data types chosen to develop machine learning algorithms, the structure of the training model, the human annotators working on it, and the visualization of the output.

The following are the consequences of biased AI:

### Impact on People
Incorrect data input or tainted [data labeling](https://www.suntec.ai/data-labeling-services.html) will lead to a similar training model. The AI research and data labeling community lack skilled professionals. Diversifying data as per users is another challenge. For instance, if a company decides to engage an AI recruitment model, the algorithms will source data from previous hirings of the firm. Now, if the firm previously hired more men, the training model will be tainted towards preferring men's resumes.
Taking another example for AI in autonomous vehicles, a human error in placing a parking sign at the wrong place will result in the AI model considering it legitimate parking.

### Impact on Economic Decision-making 
AI has found usage in insurance and lending operations. It is supposed to replace human economists in many areas. But, the limited computation capacity of AI systems, depending on the respective data annotation, can mislead economic decisions. Similarly, as economists, AI systems cannot distinguish between theories or apply them to data and analyze results.

Thereby, using AI in economics tends to have a bias as per information input. Its inability to use statistics as per the situation is another major disadvantage. In such a case, an AI system working on economics cannot present a result for a diversified user base. 

### Impact on Society
Due to the deep rooting biases, AI poses a serious threat to societal balance. For instance, there have been incidents of Mortgage AIs charging excessive interest rates from black American people, facial recognition software misidentifying women with darker skin tones, etc. Misleading data labeling due to assumptions or prejudiced mindsets has emerged as a common flaw in diversified applications of AI. 

## Promoting Responsible AI
Thanks to AI bias incidents, corporate leaders and authorities are aware of the possibility of hidden, unfair processes in their systems. They understand how unchecked can damage the business in more ways than one. Without proper regularization and guidelines from governments, biased AI with prejudiced data annotation is deemed to harm the social fabric and individual rights.

Companies like Google and Microsoft have called for the regularization of AI. The federal trade commission has also warned companies against AI bias with penalties for defaults. Many other companies too acknowledge the importance of justified data usage. However, the efforts, in this case, must be taken at a very atomic level. For instance, businesses that outsource [data annotation services](https://www.data-entry-india.com/data-support-for-ai-ml.html) or hire remote resources for content moderation must take into account the vendor’s policies approach towards data bias. 

## Conclusion
We are looking at AI as a substitute for human intelligence. But, getting there is challenging in a million ways. The chances of accidental misuse or planned sabotage (regardless of the objective) can be catastrophic to every person involved with an AI/ML model at any level. Therefore, it is important for companies to proactively identify, monitor, and mitigate potential faults in automated decision-making systems.