Organisations need to adopt basic principles for responsible to gain competitive advantage; they must also design systems that can effectively deal with biases
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With huge advancements in data handling, computational power and an increased need to handle complexity, real-life use cases leveraging () have seen a sudden upsurge over the past few years. has certainly increased the financial performance of companies, customer experiences and quality of products.
At the same time, the need to ensure responsible use of has also increased. Organisations are recognising the need to develop and operate systems with fairness, without any racial, gender or other biases, and to take care of safety, privacy and society at large. These elements are giving rise to one of the most important debates in the world of today—how to ensure ‘responsible ’ or RAI.
Private organisations, governments and international bodies are coming together to measure and analyse the technical and societal impact of systems, and are drafting principles and regulations to curb these biases. Most companies are yet to achieve RAI adoption. In this article, we investigate the tangible actions that companies should take while implementing RAI programs.
With an increase in the number of -based use cases, we are also witnessing the bias that these systems can show in decision-making. For example, there have been instances where -based hiring systems have given preference to men over women for technical roles like software development. There have also been instances when -based health care systems have tagged white people with high-risk scores, attributed to systems deducing results using cost of healthcare as an input and considering dark skin-toned people incapable of paying for high standard healthcare.
These risks are even higher for a country like India, where we have diverse cultures and the -systems may inherently imbibe these regional or caste biases. Companies across the globe have recognised the need to develop and operate systems with fairness and without any biases, while ensuring the safety and privacy of the society at large. […]
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Continue reading: https://swisscognitive.ch/2021/08/09/responsible-ai-2/