AI is very advanced these days in that its primary objective is to mimic humans through data analysis and machine learning. Worse still, the development of AI technologies has uncovered thorny issues including the perpetuation of racial bias in AI-produced content. Business insider provided some cases of the racial biases built by AI systems with these few professionals who are in technology industry as the example. Many of these biases represent a huge problem and deserve priority action.
There are, however, some cases where AI isn’t at its best and this could include something tragic such as the fall of Tay, an AI from Microsoft that happened in 2016. Using social networking platforms like Twitter, the designer of Tay, a bot, was able to learn unwanted language after encouraging users to engage in offensive interactions, causing the bot to be terminated just after 1 day. Even though there has been the development of the field of AI since the 1970s, the main issues associated with it have not been totally eradicated yet. This case testifies the necessity of fighting AI racial bias during designing the process.
Current concerns and industry response
In the latest update to an IEEE Spectrum article, Jay Wolcott, the founder of Knowbl, a generative AI Company, Brought up this crucial question of what limits AI systems must attempt to be controlled with what they decide to involve themselves. On the other hand, this issue may disclose a more serious problem when the AI-supposed perfectly designed future and the real-life story could be confronted by the side effects of AI. With the advancement of AI in many fields, in particular the question of coming up with an all-around strategy to deal with racial biases, deserves more attention.
To eliminate racial discrimination in the field of Artificial Intelligence, industries should place more emphasis on diversity and inclusion during the design of AI solutions and the training of the datasets. Diverse voices can lead to the identification of one-sidedness, or the unconscious bias even within the process of building new products. As an additional point, rigorous ethical standards and governance systems are the shields that keep AI safe once they are deployed. Collaborative activities between tech companies, governmental institutions, and advocacy associations target their creation in the future of a digitally fair and equitable AI realm.
Ensuring transparency and accountability
Transparency in AI algorithms is the key to the effective and well-mannered implementation of AI which assists in the removal of AI algorithms biases. Organizations ought to be open about how AI systems reach their decision which should also include ways for complaints and accountability. Through systematic audits and evaluations of injustices, we can detect and challenge the biases that are re-adapted from generation to generation. These key players contribute to the establishment of the required belief and confidence in AI technology by highlighting the importance of transparency and accountability.
Since the bias of race in AI opens much problems in ethical and fair use of this technology, it is the most priority issue for its further development. Although the benefit of artificial intelligence for humans is enormous, combating bias is the main task. With the idea of diversity in mind, injecting ethics into the AI environment, and promoting openness and transparency, the stakeholders can have a system that is inclusive. Collaboration of the industry accompanied by any necessary corrective measures that will eliminate racial bias and holding the principles of fairness and equality in AI are of utmost importance.
News sourced from a Business Insider article
