Algorithmic thinking has its place.
Whilst the article was written for a financial audience, it signals a timely reminder to all of us. There are lots of stories pointing out that machine learning output is only as good as the training data used to create the relevant algorithm. Therefore, since the training data is insufficiently general, the output is biased. The cry also goes up, algorithms are black boxes. So, because we don’t know how they came to a conclusion, we therefore cannot trust them. In summary, algorithmic thinking is flawed.
But humans can be every bit as biased and opaque. Moreover, humans often work with less data and can be unpredictable. At least an algorithm, which is just a set of instructions, will stick to its script. The real point is that neither we, nor any machine we make, can tell the future. All decision-making is inherently uncertain. Algorithms will, therefore, never be perfect. But they can be of help. Use them accordingly.
Link to article: https://www.ft.com/content/73e00e3c-1a2f-11e9-b93e-f4351a53f1c3
You might also like to browse other AI articles: https://www.thesentientrobot.com/category/ai/ai-articles/
