Articles: fairness and accuracy in FS algorithms

TL;DR • Knowing the basics of substantive testing vs. controls testing can help you determine if the...

TL;DR • Ongoing education helps everyone understand their role in responsibly developing and using a...

TL;DR • Outcome-focused accuracy reviews directly verify results, offering more robust assurance tha...

TL;DR • Start with five key items: Algorithm Inventory; Risk Assessment; Policies (with Procedures, ...

TL;DR • The terminology – “audit” vs “review” - is important, but clarity about deliverables is more...

TL;DR • Banks and insurers are increasingly using external data; using them beyond their intended pu...

TL;DR • Banks and insurers sometimes focus on business concerns and regulatory matters in assessing ...

TL;DR • Effective change management for algorithmic systems requires a tailored approach that goes b...

TL;DR • Proper deprovisioning of user access helps maintain algorithm integrity. • Neglecting this c...

TL;DR • Legislation and standards are helpful but not sufficient for ensuring algorithmic integrity....

TL;DR • While generative AI dominates discussions, established algorithms still drive core business ...

TL;DR • Postcodes can inadvertently serve as proxies for protected attributes like gender, disabilit...

TL;DR • Algorithm integrity requires a balance between preventing access and ensuring access. • Over...

TL;DR • Algorithmic fairness does not mean treating everyone identically. • Fair algorithms should a...

TL;DR • This article proposes breaking down algorithm integrity considerations into 10 key aspects, ...

TL;DR • AI and algorithm audit guidelines vary widely and may not be universally applicable. • Relev...

TL;DR • The motivations behind an audit (need vs. want) significantly impact its effectiveness and v...

TL;DR • Undue reliance on audit reports can create a false sense of security. • Ensure auditor indep...

Many operational decisions are now heavily reliant on automation - data, models, rules etc. We’re al...