By: Manasa Ramakrishnan (Emeritus)
In his bestseller, I, Robot, sci-fi author Issac Asimov established the three laws of robotics. The first law states that a robot should not harm a human, or by inaction let a human come to harm. The second law states that no instruction from a human shall be disobeyed. The third law states that a robot shall avoid actions or situations that could cause self-harm unless this was done to follow law one or two. Over time Asimov’s laws have become the foundation for all things AI, even in the corporate world. The laws have also become a safety feature for those in the field of Artificial Intelligence (AI) and the base for a trending discussion: How important is ethical AI?
While there has been a great deal of discussion about the use of AI across sectors, very few conscious efforts have been adopted to implement ethical AI practices. Wondering why ethical practices in AI are important now more than ever? Emeritus recently partnered with Women in AI to conduct a webinar on Data Privacy, Trust, and Ethical AI. Bhuva Subram, Women in AI’s North American regional head was joined by Caroline McCaffery, co-founder & CEO of ClearOPS Inc., and Yang Cheung, co-founder & CPO of One Creation Corporation. The discussion revolved around the latest trends in artificial intelligence, machine learning, data science, and more importantly, integration with UN Sustainable Development Goals (SDGs) for collective global societal impact.
What is Ethical AI?
Companies around the world have adopted AI to advance their services and get a competitive advantage. This is happening at a velocious pace. According to experts at Harvard, it is vital to monitor, control, and humanize this growth for the best long-term results. Ethical AI implies adopting AI in a way that is responsible, accountable, and most importantly transparent. It definitely includes abiding by laws, regulations, norms, organizational values, and consumer expectations.
Of late, ethical AI also includes protecting data, especially not letting out biased results. Moreover, every data-based decision must be justified and explainable…
