Models allow us to expose, explore, and excavate our assumptions. This is especially true for the sorts of assumptions that concern scholars of marginalization and resistance, who aim to show how ...
La Singularidad Reflexiva: Derivas Identitarias en la Topología Probabilística de Modelos Generativos
Introspección asistida por entropía
Redecentralization
A thought experiment! Let's reimagine the web with r-selected thinking.
A sampling model of social judgment.
Human perspectives on AI · Global Voices
This GV Spotlight edition will explore how the use of, promotion of, and resistance to artificial intelligence is playing out for the Global Majority.
Algorithmic monoculture and social welfare
Significance Algorithmic monoculture is a growing concern in the use of algorithms for high-stakes screening decisions in areas such as employment and lending. If many firms use the same algorithm, even if it is more accurate than the alternatives, the resulting “monoculture” may be susceptible to correlated failures, much as a monocultural system is in biological settings. To investigate this concern, we develop a model of selection under monoculture. We find that even without any assumption of shocks or correlated failures—i.e., under “normal operations”—the quality of decisions may decrease when multiple firms use the same algorithm. Thus, the introduction of a more accurate algorithm may decrease social welfare—a kind of “Braess’ paradox” for algorithmic decision-making. , As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, lending, and other domains, concerns have been raised about the effects of algorithmic monoculture, in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. Here, we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents. Unexpected shocks are therefore not needed to expose the risks of monoculture; it can hurt accuracy even under “normal” operations and even for algorithms that are more accurate when used by only a single decision-maker. Our results rely on minimal assumptions and involve the development of a probabilistic framework for analyzing systems that use multiple noisy estimates of a set of alternatives.
Algorithmic monoculture and social welfare
Significance Algorithmic monoculture is a growing concern in the use of algorithms for high-stakes screening decisions in areas such as employment and lending. If many firms use the same algorithm, even if it is more accurate than the alternatives, the resulting “monoculture” may be susceptible to correlated failures, much as a monocultural system is in biological settings. To investigate this concern, we develop a model of selection under monoculture. We find that even without any assumption of shocks or correlated failures—i.e., under “normal operations”—the quality of decisions may decrease when multiple firms use the same algorithm. Thus, the introduction of a more accurate algorithm may decrease social welfare—a kind of “Braess’ paradox” for algorithmic decision-making. , As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, lending, and other domains, concerns have been raised about the effects of algorithmic monoculture, in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. Here, we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents. Unexpected shocks are therefore not needed to expose the risks of monoculture; it can hurt accuracy even under “normal” operations and even for algorithms that are more accurate when used by only a single decision-maker. Our results rely on minimal assumptions and involve the development of a probabilistic framework for analyzing systems that use multiple noisy estimates of a set of alternatives.
Resisting Racial Capitalism: An Antipolitical Theory of Refusal
An Antipolitical Theory of Refusal
Differences Between Tight and Loose Cultures: A 33-Nation Study
The differences across cultures in the enforcement of conformity may reflect their specific histories. , With data from 33 nations, we illustrate the differences between cultures that are tight (have many strong norms and a low tolerance of deviant behavior) versus loose (have weak social norms and a high tolerance of deviant behavior). Tightness-looseness is part of a complex, loosely integrated multilevel system that comprises distal ecological and historical threats (e.g., high population density, resource scarcity, a history of territorial conflict, and disease and environmental threats), broad versus narrow socialization in societal institutions (e.g., autocracy, media regulations), the strength of everyday recurring situations, and micro-level psychological affordances (e.g., prevention self-guides, high regulatory strength, need for structure). This research advances knowledge that can foster cross-cultural understanding in a world of increasing global interdependence and has implications for modeling cultural change.
How social norms emerge: The interindividual actor–critic.
Epistemic Alienation and the Division of Labor
The division of cognitive labor leads to what Barry has recently called epistemic alienation: a problematic separation of individuals from epistemic goods. According to Barry, individuals are alienated from epistemic virtues because an efficient division of cognitive labor requires them to manifest a lack of virtues. I argue that this is a mistaken diagnosis of the source of epistemic alienation. Participating in high-functioning collectives does not prevent individuals from being robustly virtuous; rather, our cognitive limitations make it impossible for us to live up to the highest standards of epistemic conduct. Collectives transcend these cognitive limitations to achieve what individuals cannot by harnessing those same limitations in their members. Furthermore, I argue that we should distinguish the epistemic weaknesses that facilitate the division of cognitive labor from those that result from the division of labor. Only the latter are aspects of epistemic alienation. By dividing our cognitive labor over larger populations of agents and artifacts, we have massively accelerated our rate of epistemic productivity but have thereby created conditions that are variously inhospitable for the thinking of individuals by alienating them from the products, processes, and environments of inquiry. These forms of separation are problematic insofar as they amplify intellectual vices, incapacitate reason, and induce cognitive biases.
Reversing Assumptions Technique — Think Jar Collective
Think Jar Collective contributor and creativity expert Michael Michalko shares a technique to challenge our own assumptions and in the process spark new thinking.
How the Anthropic saga could threaten American AI dominance
The shutdown of top AI models reverberates abroad.
Fairness is what the powerful ‘can get away with’ study shows
The willingness of those in power to act fairly depends on how easily others can collectively push back against unfair treatment
