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Coding with Responsible AI in Mind

Updated: Aug 24, 2021

By Patricia Franklin | Chief Learning & Content Officer at Flerish

Putting Responsible AI/ML Into Practice, an Athena Technology Special Interest Group leadership program sponsored by Intuit, will take place 5-6 PM on August 12th. To attend this virtual event, please register here.

Walk away with actionable insights into the global AI market valued at $62.35 billion that is expected to expand at a compound annual growth rate (CAGR) of 40.2% from 2021 to 2028.[1] Let Athena guide you through this money maze of technology that breaks good and bad at every turn.

When Francis Bacon, wrote in the late 16th century that “knowledge itself is power” he was saying that knowledge is the cornerstone of reputation and influence. The matter of who holds that power is at the crux of the debate over the ethics of artificial intelligence (AI) and machine learning (ML).

An AI/ML algorithm behaves ethically not because it chooses to, but because it’s programmed to do so. If those doing the programming are predominantly of a certain gender and race, political persuasion and/or sexual orientation, their perceived and unconscious biases inform how their code is written.

As AI/ML becomes more prevalent, Responsible AI raises awareness of ethics and diversity that can help to limit coding bias. Perceived or unconscious bias might arise when 78.9% of computer programmers in the US are male and 65% are white and over 40. [2]

As engineers, data and computer scientists, and technologists create worthy solutions that solve today’s toughest problems, we must ensure development of responsible AI/ML techniques that address ethics, coding bias, the importance of diversity, and public sector policies for promoting and regulating AI/ML.

Engaging with stakeholders from diverse backgrounds is an essential step in the process of improving Responsible AI. Actively listening to and addressing the concerns of people with different perspectives throughout the design, deployment, and adoption of AI systems can help identify and mitigate unintended consequences – a key finding of the World Economic Forum’s Responsible Use of Technology project community. This approach may also lead to the creation of better products that serve a larger market.

To advance our understanding of these issues, Athena’s Technical Special Interest Group presents Putting Responsible AI/ML Into Practice, featuring Diane Chang, Data Scientist, Intuit; Gioia Messinger, Founder, LinkedObjects; Blythe Towal, ML Tech Architect and Tech Lead, Shield AI and Anne-Lise Thieblemont, VP, Qualcomm. Register here for this August 12th virtual event at 5-6pm PT.



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