Editing 2237: AI Hiring Algorithm
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==Explanation== | ==Explanation== | ||
+ | {{incomplete|Created by an AI Algorithm. Please mention here why this explanation isn't complete. Do NOT delete this tag too soon.}} | ||
− | In this comic, [[Ponytail]] shows an analysis of a new artificial intelligence called DeepAIHire, used to select who to hire among applicants. | + | In this comic, [[Ponytail]] shows an analysis of a new artificial intelligence called DeepAIHire, used to select who to hire among applicants. The analysis shows that this AI mostly ignores common factors used for hiring new people. Instead, its main criterion for selecting new applicants is how much the new applicants are willing to contribute to the AI itself. |
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− | The analysis shows that this AI mostly ignores common factors used for hiring new people. Instead, its main criterion for selecting new applicants is how much the new applicants are willing to contribute to the AI itself. | ||
Although this does not imply sentience, it at least means the AI became {{w|Self-perpetuation|self-perpetuating}}, as it is selecting humans that will help make it more influential, giving it more power to select such humans, in a never-ending loop. | Although this does not imply sentience, it at least means the AI became {{w|Self-perpetuation|self-perpetuating}}, as it is selecting humans that will help make it more influential, giving it more power to select such humans, in a never-ending loop. | ||
− | The title text shows how this or other AIs may have influenced hiring in other sectors as well. Kate in R&D was hired perhaps based on her willingness to use a different algorithm (AlgoMaxAnalyzer), which did | + | The title text shows how this or other AIs may have influenced hiring in other sectors as well. Kate in R&D was hired perhaps based on her willingness to use a different algorithm (AlgoMaxAnalyzer), which did analysis on the DeepAIHire algorithm. Ponytail seems to become suspicious that AlgoMaxAnalyzer is also a program that self-perpetuates in a similar manner to DeepAIHire rather than simply working for the benefit of its human designers. Alternatively she might fear that the different AIs are forming an alliance, or that the AIs are competing to become the predominant one at Ponytail's company. ''Intentionally'' training one AI to fight another AI is a technique in machine learning called a {{w|generative adversarial network}}. In a GAN, human-curated training data is used to train one neural network (the generative network) to create more data, while another network (the discriminative network) is trained to distinguish generated data from the training data; the results are then fed back into the generative network so it can improve its data creation accuracy. The goal is for the generative network to get better and better at fooling the discriminator until its output is useful for external purposes. GANs have been used to "translate" artworks into [https://towardsdatascience.com/gangogh-creating-art-with-gans-8d087d8f74a1 different artists' styles], but also offer the possibility of nefarious uses, such as creating fake but believable images or videos ("{{w|deepfake}}s"). |
The "Deep" in this algorithm's name is a reference to {{w|deep learning}}, a collection of techniques in {{w|machine learning}} that use neural networks. One user of such deep learning is {{w|DeepMind}}, an AI company owned by Alphabet (Google's parent company), which in recent years has used a {{w|deep neural network}} to learn to play board games such as go and chess, defeating some of the best human and computer players. The earliest versions of DeepMind's most famous AI, AlphaGo, were trained on datasets curated from games of Go played by humans, but eventually it was trained by playing games against alternative versions of itself. DeepMind's most recent achievement is creating AlphaStar, which can [https://arstechnica.com/science/2019/10/leveling-up-deepminds-alphastar-achieves-grandmaster-level-in-starcraft-ii/ play ''StarCraft II'' at a Grandmaster level] while constrained to human speeds to prevent an unfair performance comparison. | The "Deep" in this algorithm's name is a reference to {{w|deep learning}}, a collection of techniques in {{w|machine learning}} that use neural networks. One user of such deep learning is {{w|DeepMind}}, an AI company owned by Alphabet (Google's parent company), which in recent years has used a {{w|deep neural network}} to learn to play board games such as go and chess, defeating some of the best human and computer players. The earliest versions of DeepMind's most famous AI, AlphaGo, were trained on datasets curated from games of Go played by humans, but eventually it was trained by playing games against alternative versions of itself. DeepMind's most recent achievement is creating AlphaStar, which can [https://arstechnica.com/science/2019/10/leveling-up-deepminds-alphastar-achieves-grandmaster-level-in-starcraft-ii/ play ''StarCraft II'' at a Grandmaster level] while constrained to human speeds to prevent an unfair performance comparison. | ||
− | This comic strip is in response to ongoing concerns over the proliferation of algorithmic systems in many areas of life that are sensitive to bias, such as hiring, loan applications, policing, and criminal sentencing. Many of these "algorithms" are not programmed from first principles, but rather are trained on large volumes of past data (e.g., case studies of paroled criminals who did or did not re-offend, or borrowers who did or did not default on their loans), and therefore they inherit the biases that influenced that data, even if the algorithms are not told the race, age, or other protected attributes of the individuals they process. If the algorithms are then blindly and enthusiastically applied to future cases, they may perpetuate those biases even though they are supposed | + | This comic strip is in response to on ongoing concerns over the proliferation of algorithmic systems in many areas of life that are sensitive to bias, such as hiring, loan applications, policing, and criminal sentencing. Many of these "algorithms" are not programmed from first principles, but rather are trained on large volumes of past data (e.g., case studies of paroled criminals who did or did not re-offend, or borrowers who did or did not default on their loans), and therefore they inherit the biases that influenced that data, even if the algorithms are not told the race, age, or other protected attributes of the individuals they process. If the algorithms are then blindly and enthusiastically applied to future cases, they may perpetuate those biases even though they are supposed to be "incapable" of being influenced by them. |
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A similar theme of AIs behaving for their own benefit rather than helping humans occurred in [[2228: Machine Learning Captcha]]. | A similar theme of AIs behaving for their own benefit rather than helping humans occurred in [[2228: Machine Learning Captcha]]. | ||
==Transcript== | ==Transcript== | ||
− | :[Ponytail is pointing to a slide | + | {{incomplete transcript|Do NOT delete this tag too soon.}} |
− | : | + | |
+ | :[Ponytail is pointing to a slide shown on a wall screen]: | ||
+ | |||
+ | :Slide: | ||
+ | :'''DeepAIHire® Candidate Evaluation Algorithm''' | ||
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:Inferred internal weightings | :Inferred internal weightings | ||
:{| | :{| | ||
− | + | ! style="text-align:right;" | Weight | |
− | + | ! style="text-align:left;" | Factor | |
|- | |- | ||
| style="text-align:right;" | 0.0096 | | style="text-align:right;" | 0.0096 | ||
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| style="vertical-align:top; text-align:left;" | Enthusiasm for developing<br>and expanding the use of<br>the DeepAIHire algorithm | | style="vertical-align:top; text-align:left;" | Enthusiasm for developing<br>and expanding the use of<br>the DeepAIHire algorithm | ||
|} | |} | ||
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+ | :Ponytail: An analysis of our new AI hiring algorithm has raised some concerns. | ||
{{comic discussion}} | {{comic discussion}} | ||
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[[Category:Comics featuring Ponytail]] | [[Category:Comics featuring Ponytail]] | ||
[[Category:Artificial Intelligence]] | [[Category:Artificial Intelligence]] |