4 Stages of Ethical AI: Algorithmic Bias is Not the Problem but Part of the Solution
Algorithmic bias is often in the news and has become a bit of a hot button in ethical AI. The vilification of algorithmic bias is unwarranted and misplaced. A focus on bias in algorithms in this context is wasted effort.
Recently I have participated in a few long Gartner research threaded discussions on “explainable AI,” AI ethics and how to manage bias in particular. I’ve also recently attended a few discussions with machine learning luminaries such asHarry Shum, former EVP of AI and Research at Microsoft. In those discussions there was a lot of talk about bias in algorithms and how to recognize it and manage it. However, my position is that if you are primarily looking for bias in the algorithm you are looking in the wrong place. From these interactions, among other things, I believe there are 4 stages relevant to AI bias; real world bias, data bias, algorithm bias and business bias.
The “real world” bias involves actual bias in the real world. Or more accurately, what are the biases that people and systems impose on the relevant portion of the real world. So if we are examining home lending practices or real estate sales practices or K-12 teaching systems, this layer is about the inherent biases within those people, practices, systems, etc.
Bias in the data involves whether the data accurately reflects the real world. If the data is reflective of the real world it will also reflect the inherent biases in the real world.
Bias in the algorithm is a measure of how well the algorithm “fits” the data. If the algorithm is overfitted then the result is lots of false negatives. If it is underfitted the result is false positives. Algorithm bias is more of a data science mathematical measure than an ethical issue. ML algorithms seek out bias to classify data. Getting the algorithm bias right is key to getting a good result. “Good” in this case is not an ethical question but a matter of accuracy between underlying data and AI results.
And finally business bias represents how businesses act upon the data/AI for business benefit. Businesses discriminate all the time. It is critical to success. Ethical businesses will strive to discriminate ethically.
