There is no doubt that corporations are now understanding the need and relevance of AI for excelling in business. This is evident with the latest AI adoption rates or the adoption of AI tools  in almost every thinkable industry. Then be it finance, telecom, aviation, healthcare, automotive and or any other major sector, AI is setting its foot and is there to stay. Even with the debate on its worth and associated dangers on, the significance of the technology is well communicated.

However, with every innovation on new technology adoption there are few risks involved which need attention and resolutions. Same is the case with AI which has few concerns among the stakeholders over its large scale adoption and involvement in aspects where people have direct relation. And if we actually look at it, every usage of the technology eventually leads to a situation where humans are getting affected in one way or the other, then be it the desired positive effects or be it the risks involved.

So without much ado let's have a look at the concerns that are mostly associated with the usage of AI in various areas.

• Cybersecurity
• Regulatory Compliance
• Personal/Individual Privacy
• Explainability
• Workforce/labour Displacement
• Organizational Reputation
• Physical Safety
• Equity and Fairness
• National Security
• Political Stability

According to McKinsey Global Institute’s research by 2030, AI could deliver additional global economic output of $13 trillion per year. Which conveys that AI is a double edged Sword and hence we shall be cautious and responsible around its usage. AI is generating various benefits along with some unwanted ones like the above mentioned ones. With the avid usage of AI in areas of medicine and security, taking chances and ignorance is not an option. 

This is why a clear understanding of the involved risks is of utmost importance. 

According to the 2021 AI Index Report by Stanford University cybersecurity was the biggest risk perceived by organizations around adopting AI. This was followed by regulatory compliance as another big inhibition that businesses encountered. This scenario has remained the same in last two years with cybersecurity, regulatory compliance, and privacy as the major risk factors considered by businesses around AI. 

Even with fairness, equity, and ethics being the hot topics in AI research, it seems businesses have yet not perceived it as a major concern area. These issues are not getting the attention as they should be among the respondents in the survey conducted by McKinsey.

Security issues and technology troubles are still the bigger concerns among businesses whereas we need equivalent focus to be brought to the issues of ethics, equity, bias, and fairness. As beyond the internal issues of security and so we eventually need AI to be adopted for the benefit of human race in the bigger picture. According to the 2021 AI Index Report By Stanford University. even with approx. 24% of respondents considering equity and fairness as a risk in 2020, only approx. 14% are taking steps to mitigate the same.

This also means that for various industries the involved risks might be different with varied priorities specific to that business or industry. For financial institutions cybersecurity and privacy definitely are critical in terms of AI adoption while for healthcare explainability and workforce displacement might be a concern. With law however, equity and fairness is of utmost importance.

There are various ways we can actually work towards mitigating these issues. Conceptualisation, data management, model development, model implementation, and model usage and decision making broadly are the action areas that can help organisations control and mange risks. Reinforcement based on specific controls depending on the nature of the risk is important for resolutions and finding a way out.

The right balance between innovation and risk identification and management is important here. 

The fact that more than half of respondents considered cybersecurity as a significant risk point to the need of more awareness and strategic actions. Industry leaders who are working towards mitigating these risks should cultivate more skills around the AI and shall try engaging the whole organisation. This would lead to ore involvement along with empowerment and a responsible attitude towards AI. Risk identification is the key to narrow down and strategize policies and regulatory framework along with self-policing. In the end it is us who have to decide where to draw a line and have ethics deep imbibed in the algorithms.

Image by Gerd Altmann from Pixabay