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[ November 14, 2021 ] Protected: AI Waves: Applying AI in Business AI
[ October 19, 2021 ] Algorithmic Bias | Towards greater Transparency, Fairness and Representation in automated systems design AI
[ May 10, 2021 ] AI | FinTech – Forget scorecards and turn your credit scoring into a money-making machine [VIDEO] AI
[ February 25, 2021 ] 2021 UK Drone Ownership – The Law and Best Practice Tips 2021
[ February 19, 2021 ] New AI Data Analytics toolkit launched by ICO AI
[ January 25, 2021 ] Blockchain E – Salon: What’s all the hype around Blockchain about? [VIDEO] Blockchain
[ January 9, 2021 ] AN INTRODUCTION TO BLOCKCHAIN AND CRYPTO ASSETS [VIDEO] Blockchain
[ July 23, 2020 ] DOES YOUR AI PASS THE EUROPEAN COMMISSION RISK-ASSESSMENT FRAMEWORK? AI
[ July 23, 2020 ] COVID-19 and The Future of Work with F-LEX Carreer
[ June 6, 2020 ] Legal Technology and its applications and implications for regulation and practice event

Protected: AI Waves: Applying AI in Business

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Algorithmic Bias | Towards greater Transparency, Fairness and Representation in automated systems design

October 19, 2021 0

Could you be a victim of algorithmic discrimination without even realising it?

Algorithms are increasingly being used in more settings than ever before and they are often relied upon to make snap decisions on future events, based of patterns found in historic data.

Understandably though, whilst the potential for automation and predictive analytics in decision making is vast; A rise in the adoption of algorithms has also brought with it increased risks of over reliance and social harm as algorithms continue to take an ever more prominent role in our lives.

AMZN.O’s recruiting algorithm and the the use of AI algorithms by the police are just two examples that have helped cast a spotlight on the falibility of automated system design. Most importantly, depending on how they are created, algorithmic errors can to not only reflect present and historic social issues, but aggravate them also. These risks and effects, will need to be closely monitored to prevent repeating human errors in an irreversible way.

Join WiBrief In this latest article where adopt a case study approach to deep dive into recent high profile cases of algorithmic use and misuse:

We cover:
1. What are algorithms,
2. The algorithmic problem – systematising bias in code,
3. What is ‘Algorithmic Bias’ and the different types of bias,
4. AMZN.O’S recruitment Case study
5. The Legal Context
6.The Social Context
7. Outlook: The need for standardising an approach for detecting and measuring algorithmic Bias.
8. Key takeaways

Read the full article here:

https://wibrief.com/?p=7539

If you enjoyed this article, then look out for our upcoming coverage of the recent changes in AI Regulations, covered by AI WAVES

Read the full article here. …

AI | FinTech – Forget scorecards and turn your credit scoring into a money-making machine [VIDEO]

May 10, 2021 0

WiredBrief are delighted to bring to you an exciting event hosted by AI Waves, which looks at Nextbanks AI driven FinTech credit scoring solution and its ground-breaking implications for the financial sector. …

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