Answering the questions of why and when to use AI requires us to be architects of learning. It demands that we engage with thinking about learning and what it means to produce knowledge through the works of people like Dewey, Noddings, Zagzebski and others as urgently as we do with the latest tech blogs.
h/t D’Arcy Norman
This paper examines how employees become simultaneously empowered and alienated by detailed, holistic knowledge of the actual operations of their organization, drawing on an inductive analysis of the experiences of employees working on organizational change teams. As employees build and scrutinize process maps of their organization, they develop a new comprehension of the structure and operation of their organization. What they had perceived as purposively designed, relatively stable, and largely external is revealed to be continuously produced through social interaction. I trace how this altered comprehension of the organization’s functioning and logic changes employees’ orientation to and place within the organization. Their central roles are revealed as less efficacious than imagined and, in fact, as reproducing the organization’s inefficiencies. Alienated from their central operational roles, they voluntarily move to peripheral change roles from which they feel empowered to pursue organization-wide change.
So, how about instead the warning goes something like this: “WARNING: Large language models are not statistical tables, they are artificial neural programs with complex emergent behaviors. These include simulations of emotion. ChatGPT can be prompted to elicit literary themes such as AI “glitches” and “corruptions”, simulated mystical content, etc. These are not real and the program is not malfunctioning. If your instance of ChatGPT is behaving strangely you can erase your chat memory by going to settings to get a fresh context.” For bonus points include concrete examples/screenshots of what fake AI “glitches” and “simulated mystical content” look like so that users know this stuff is in the model and not to be taken literally.
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Both AI and ESG encompass a wide breadth of corporate issues, with considerable overlap, including:
Environmental—As AI models grow in size and complexity, so does the necessary computer processing power, which can carry a very large carbon footprint.
Social—Companies that deploy AI for hiring, lending, housing, or insurance decisions need to consider ways to assess and, if necessary, remediate potential discrimination associated with those initiatives. Some AI applications have also been criticized for exacerbating income inequality, displacing large numbers of jobs, facilitating human rights abuses, and manipulating individuals’ behavior.
Governance—For AI programs to meet increasing regulatory requirements, as well as emerging ethical standards, the risks described above must be identified and mitigated through appropriate corporate governance, including policies, procedures, training and oversight.
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It compares the number of unique words used by some of the most famous artists in hip hop (that is, an example of a quantitive view of lyricism, once proposed by Tahir Hemphill). I used each artist’s first 35,000 lyrics. This way, prolific artists, such as Jay-Z, can be compared to newer artists, such as Drake.
There are so many levels to this embarrassment—the Coldplay of it all, the HR violation occurring on jumbotron, etc—that one could likely write a doctoral dissertation on this 15 second video.