In video games or any gamified implementations of concepts, significant progress unlocks subsequent levels. The newly unlocked level builds on the existing expertise of a player and lays the foundation for the way forward. As far as Artificial Intelligence (AI) is concerned, we are in that phase, where level 2 is being unlocked gradually.
This new phase is what we call Responsible AI, where we are gradually moving away from conversations on dynamic implementations of AI concepts and tools to establishing ethics and airtight responsibilities around them.
Discussed widely in conclaves, summits, and events around the world by leading tech enthusiasts and global leaders, the concept of responsible AI is fast garnering attention in terms of policy revisits and mindset shifts.
If you’re an enterprise with AI visions and deployments, now will be the right time to eye on making your AI goals responsible. To help you get started, here are compelling reasons why this is the right time to opt for responsible AI.
The Need For Accountability In AI-driven Decision-making Processes
When an AI algorithm makes a decision or spawns a response, we assimilate and implement it blindly. Little do we analyze why a specific response was delivered in the first place and how the algorithm decided the result. With little to no accountability for the decisions taken by AI, it is on the responsible AI to put things in place and establish ground rules, protocols, and policies that hold an AI model responsible and accountable for its decisions.
With such rules, stakeholders can justify an action taken and not just go blindly with AI-generated results. From a bad reputation to even insulting lawsuits, responsible AI could save enterprises from one game-changing bad decision.
Reduced Bias
An AI learns from the data it is fed. As massive chunks of data are fed as inputs for AI algorithms to learn, there are hardly any moderation in the diversity of datasets. When left unchecked, such datasets can have a bias in them that could ultimately skew results, making them one-sided, discriminatory, or anti-inclusive.
An enterprise feeding data from its in-house employees comprising 80% men and 20% women involuntarily give rise to bias, where results mostly stem from the mindset and responses of the majority of the lot – men. If at micro levels, these are possibilities, massive training sets can cause super-intensive problems. Responsible AI keeps a check on such instances from happening by being inclusive right from the data mining or collection stages.
Whose Fault Is It Anyway?
AI is incredible when it works in favor of our intentions and goals. However, what if it has some dark implications? What if a blindly-taken decision rattles your business’s operations, reputation, and productivity? Who is to be blamed at that instance of time?
Establishing protocols and implementing responsible AI techniques helps you do away with such ambiguities in advance so there is ample scope for stakeholders to justify, prevent, or revert decisions powered by AI.
Final Thoughts
Not just these. When your enterprise implements responsible AI, it automatically helps build trust in AI and its deployment. It ensures diverse legal, ethical, societal, and environmental implications of Artificial Intelligence systems are mitigated. As we make significant progress in AI and work on implementing AI in crucial industries like healthcare, pharma, automotive, and more, the timing cannot be more appropriate than now to start approaching AI the responsible way.
What do you think?
This article is written by Vinay Konanur, Senior Director – Emerging Technology, UNext Learning
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