Beyond the evil facial recognition myth - can AI play an ethical role in predictive threat detection?
The stories on facial recognition advances lean strongly towards the concern side, with a host of consequences poorly addressed during rollouts. But is there an AI-for-good role in threat prediction?
The time has come think critically about the value of AI as it stands, and whether to be concerned that a concerted effort to press it forward to true intelligence bypasses ethical questions.
What can you do with 1,000,000,000,000,000,000 Floating-point calculations per second (EXAflops)? Simulate nuclear weapons? Yes. Cure cancer? Maybe...
Retailers' attempts to solve out-of-stock and predict demand have a long history. But have we finally turned a corner? A look at AI startup NextOrbit points to new possibilities.
Variations on Dolly Parton - making things simple is pretty complicated.
Yes, health care needs AI - but maybe not in the ways we think. A new book on AI's medical potential needs a critical eye. With AI, there is always a human consequence beyond the tech storyline.
The issue of AI ethics has sharpened - ideas for governing AI and ethical oversight are gaining a foothold. But will they have any teeth? And what about the possibility that AI can oversee itself?
A new research paper from Georgia Tech takes a surprising position on algorithmic bias in hiring. Their view: we can reduce screening bias if algorithms take the impacted demographic groups into account. Here's my critique.
New York state regulators pushed back on data brokers for insurance - will other states and industries follow?
A warning letter from New York State's Department of Financial Services (DFS) raised far-reaching data privacy questions for the insurance industry. With the increasing role of algorithmic claims processing, this is an ethics debate we can't ignore.
The fall of MapR caused a rush to judgement about the future of Hadoop. To understand what this means for data initiatives, the viability of Hadoop and data lakes must be separately examined.
AI marketing literature extols the benefits of algorithmic hiring. But the problem of algorithmic bias and hiring fairness raises serious questions.
The term "data sharing" is expanding, but in a problematic way that raises flags for companies and consumers alike. Neil Raden provides a deeper context for data sharing trends, dividing them into the good, bad and ugly.