K Fukagawa

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Thanks for writing such a clear and well-structured post! The three questions helped me think through some of these key concerns around data collection and its use. I’m afraid that people analytics is becoming increasingly invasive, and the agency of employees is being diminished. In addition to addressing privacy and security concerns, companies who want to collect more data need to make a clearer case for how it would be beneficial for the employees as well, and not just the employers. Similar to the notion of ‘gender mainstreaming’ in which all policy decisions must take the implications for all genders into account, there needs to be an ’employee mainstreaming’ approach in people analytics, in which the perspectives of the employees are embedded in every company decision. Hopefully this will shift the focus from being centered around ‘performance’ and ‘productivity’ to the emotional and physical well-being of employees.

Interesting article!
I wonder to what extent HireVue provides customization to its clients. Do they alter what their algorithms consider as high-potential candidates depending on the organization and the type of role it is hiring for? For example, while agreeing with many of the concerns raised in your blog, I can imagine that facial expressions, tone of voice, and word choice would be a pretty useful indicator of a candidate’s fit for a sales job or other client-facing roles, but perhaps not as necessary if you were hiring an engineer (Of course communication skills is important regardless of the job, but I think there are varying degrees).

I would also be curious to know when and how this tool is used within the recruiting process. For example, is this the only tool that is used, or do employers supplement if with other tools or processes? Is it generally used for the initial screening process, or can it be used further down the recruiting process?

On April 13, 2020, K Fukagawa commented on Books and Movies in the era of AI :

What a fascinating topic! While this may be a chicken-and-egg issue, what I find interesting is that these algorithms reflect the fact that audiences have reached a degree of homogeneity in terms of taste and interests that they can detect a certain type of magic formula that ‘works’. As a fan of independent films, I would hope that this actually creates more impetus for creators to fight the trend and make original content that pushes conventional film plots and presentation. At the same time, I can imagine that it’s precisely because independent film studios are also cash-strapped that they would find the help of algorithms to be appealing if it can attract greater audiences and be more cost-efficient. I think this tension between originality/creativity and cost-efficiency/ROI will continue to deepen as AI technology becomes more widely deployed.