The rise of AI adoption has posed additional challenges and cyber risks in the digital space, which calls for more responsibility on technology to maintain fairness and security.
Artificial Intelligence (AI) is having increasingly higher influence on humans’ decision-making processes, and expectations are whether AI will follow the norms. Principles governing the behavior of responsible AI systems are being established on the below pillars:
All AI systems should fairly deal with people and be inclusive in coverage. In specific, they should not show any bias during operations. Historically, humans have utilized at least two major criteria for unfair treatment, including gender and race/ caste/ethnicity.
On this point, Amazon tried to develop a specific AI-algorithm for recruitment. However, the algorithm started showing less tendency to choose female candidates. Even post removing gender-specific indicators, the problem was not fixed, so the project had to be abandoned.
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Transparency and Accountability
Unlike traditional software, it is challenging to predict AI algorithms’ outcome as they change with training. This makes them comparatively less transparent, and such a “Black box” nature of AI makes it very challenging to find the source of error for any wrong prediction. This also makes it comparatively difficult to pinpoint accountability issues.
Neural networks remain the underlying technology for many voice, character, face, or other recognition systems. Unfortunately, it is more of a struggle to trace problems in neural networks, especially the deep-lying ones, than in other AI algorithms, e.g., decision making trees, etc. And latest variants of neural networks, e.g., Spiking Neural Networks, GANs (Generational Adversarial Networks), etc., continue to gather popularity.
Reliability and safety
The security and reliability of AI-driven systems have certain specific dimensions, e.g., unpredictability. In collaboration, Facebook and the Georgia Institute of Technology created bots that could negotiate, but they also ended up learning how to lie. This was never intended during programming.
Unpredictability reduces the safety and reliability of the systems. The real power of AI algorithms lies in the models and the weights of the system’s features. The biggest source of biases lies in the data on which the AI systems run; they can ruin everything explicitly or subconsciously. This happens if data carries implicit historical/ societal biases or is not uniformly sampled.
The concerns over AI ethics have resulted in multiple organizations formulating guidelines overlooking the use of AI, e.g., the European Commission’s detailed “Ethics Guidelines for Trustworthy Artificial Intelligence,” IEEE’s P7000 standards projects, and the US government’s “Roadmap for AI Policy,” etc. These contain the general guiding principles of ethics and responsibility that all AI systems should follow.
Many companies have specifically defined frameworks, guidelines, software, etc. that can help to create Responsible AI, e.g., Microsoft, PWC, Amazon, IBM, Google, Pega, Arthur, H2O, etc. Such software helps explain the model’s “Black box” behavior, bringing in transparency, mitigating bias against any identity-based groups, assessing the fairness of the systems, and keeping the data secure by constant monitoring.
Within companies, responsible AI functioning can be facilitated by imposing stringent standards through overseeing groups, ensuring diversity in teams to cascade individuals’ message. There should always be a conscious effort to bring down data biases.
Over the next two decades, machines are likely to become more autonomous in decision-making processes, and human beings will slowly cede control of their lives. The establishment of Responsible AI will eliminate biases and increase the acceptance of AI. This will allow us to create a more fair and equitable society. The unchecked growth of AI will make humans less tolerant of AI, as well as to each other.