Ethical Artificial Intelligence

Artificial Intelligence may be a tool a bit like the other - it's not inherently good or bad - it's the actors and their intents that matter here. AI is helping in healthcare and governance to improve people's lives. It is also getting used online for cheating, forgery, sowing discord, also as for advanced offensive weaponry.

Building AI with ethics may be a pertinent problem now quite ever as AI is being applied to more sectors.

Companies are not only using AI to recommend the next product to us, but they are also using it in areas that are risk-sensitive. The extent to which machine learning is employed in safety-critical applications today has made the issues of ethical AI even bigger.

We have not been ready to solve this dilemma from a person's perspective, how can we expect machines to know this?

“Growing complexity of systems and processes in business and governance, as well as the growing volume of our personal online and offline interactions - they all need AI solutions for better management. The ethics in AI, therefore, are hugely important”.

While the answer to ‘What is ethical’ varies for every industry, in basic it leads to the aspects of privacy, morality, transparency, security, and solidarity. The ethics for AI include the purpose of AI’s deployment (healthcare or warfare) and fairness in the AI’s decision-making.

But we need to make the Artificial Intelligence and Machine Learning models more ethical for people to trust it.

While Artificial Intelligence and Machine Learning are getting used to bridge the gap in many sectors, we don’t trust these models enough to give them the power to decide about life and death.

“Covid has been a raging issue for the last couple of years. There have been peripheral issues where ML was used, but not in many cases where it involved a risk of life -- despite the severity of the crisis. We don't yet trust AI and ML when it involves making decisions that affect a life”.

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As AI decisions influence and impact people’s lives at scale, it's crucial that organizations take a proactive approach to designing AI responsibly. Architecting and deploying AI models that are trustworthy, fair, and explainable is the key. The conversation about ethics and responsible AI remains evolving and no one has a definitive answer to how to move ahead. However, there have always been some best practices and things to keep in mind while designing and working with Artificial Intelligence and Machine Learning.

“Organizations got to adopt proven qualitative and quantitative techniques to assess potential risks and mitigate bias in AI models. Deploying the right set of tools and establishing practices to thoroughly and continuously investigate sources of bias and understand the trade-offs and impacts of fairness decisions is critical.”