It’s time for enterprises to take a long, hard look at AI Ethics
- Only 35% of
global consumerstrust how AI is being implemented by organisations.
- AI outcomes can be biased or discriminatory and do not take into consideration the plurality and diversity of societies.
- Responsible AI is becoming the new business imperative.
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This is one of those instances when we tend to agree with overstatements like ‘AI is an existential threat to humanity’.
As Artificial Intelligence (AI) penetrates every aspect of our lives, and AI algorithms decide what we see, how we interact and what we buy, a million-dollar question arises – what can organisations do to ensure the use of responsible AI? The
AI ethics & governance: A top priority
The answers are not easy to come by. The UNESCO’s Recommendation on the Ethics of Artificial Intelligence was adopted by its 193 member states as recently as November 2021.
It is aimed at ‘defining values, principles and policies that will guide countries in building legal frameworks to ensure that AI is deployed as a force for the common good.’ While it marked a great leap in the right direction, there is still a lot that needs to be done in this area.
As businesses scale up the adoption of AI across the organisation, they must be willing to accept the responsibility of producing outcomes that are transparent and unbiased, with human interest at the core. Businesses must have a razor-sharp focus on AI governance, ethics, and evolving regulations. Without adequate data governance, enterprises risk losing reputation, customers and tons of money due to non-compliance.
The fact is that only 35% of global consumers trust how AI is being implemented by organisations, while 77% think organisations must be held accountable for their misuse of AI, according to Accenture 2022 Tech Vision research.
The pitfalls of unintended bias
Often, AI is not just about finding new business opportunities, understanding customers, and improving the topline. UNESCO has been quite vocal about why businesses must focus on human-centred AI and has warned that the technology poses an unprecedented ethical dilemma.
“We are seeing lack of transparency, gender and ethnic bias, grave threats to privacy, dignity and agency, the danger of mass surveillance, and a growing use of unreliable AI technologies in law enforcement, to name a few,” states the agency. It has underscored how AI outcomes are often biased or discriminatory and do not take into consideration the plurality and diversity of societies.
AdvertisementBiased outcomes from AI are already widespread across industries. The financial services sector is just an example. As lending processes get automated, credit algorithms tend to throw up results that are skewed against women. AI systems are only as good as the data they are fed. Unfortunately, input data can be highly biased, leading to an ongoing cycle of biases.
Amazon was forced to do away with a ‘sexist AI’ tool for recruitment that discriminated against women. In the healthcare sector, where AI’s use has seen an exponential increase during the pandemic, there is the quintessential question of how doctors can safely rely on AI recommendations while deciding critical treatments. There is also the challenge of evolving regulatory requirements in these sectors. Autonomous vehicles have already raised several questions around data privacy, ownership and access of data.
Commitment to ethical AI
Many responsible organisations are working on building trust and transparency into their AI programs. They are leveraging more diverse data sets to overcome unintentional AI bias and the related challenges. What was so far on paper is now being put into practice by an increasing number of organisations. Responsible AI must soon become a business imperative for enterprise leaders.
Risk managers, however, acknowledge that it’s going to be an uphill task, with only about 11% of them admitting to having the full capability of assessing the risks associated with adopting AI across the enterprise, as per an Accenture global survey. The future of this technology hinges on the development of human-centric AI models and systems that are inherently trustworthy, fair, and transparent.
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