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Problem of induction

The problem of induction is a central challenge in epistemology and the philosophy of science that questions the rational justification for inductive inferences, which generalize from specific observations to broader conclusions about unobserved events, such as predicting future outcomes based on past patterns.^[1] First systematically formulated by the Scottish philosopher David Hume in the 18th century, it highlights the apparent circularity in attempting to validate inductive reasoning through either a priori demonstration or empirical evidence, as both approaches presuppose the very uniformity of nature that induction seeks to establish.^[1] Hume argued that all inferences concerning matters of fact rely on the relation of cause and effect, yet no necessary connection between causes and effects can be discerned through reason alone, nor does experience reveal more than constant conjunctions without binding necessity, leaving inductive conclusions resting on habit rather than logic.^[1] Hume's formulation, detailed in An Enquiry Concerning Human Understanding (Section IV), posits that "all reasonings concerning matter of fact seem to be founded on the relation of Cause and Effect," but "even after we have experience of the operations of cause and effect... our conclusions from that experience are not founded on reasoning."^[1] This skepticism implies that scientific laws and everyday predictions lack a non-circular foundation, threatening the reliability of empirical knowledge.^[2] The problem gained renewed attention in the 20th century through Bertrand Russell's vivid analogy that failing to solve it would make the difference between sanity and insanity indistinguishable, emphasizing its implications for distinguishing reliable science from superstition.^[2] Philosophers have proposed various responses to address Hume's challenge, though none is universally accepted. Karl Popper's falsificationism rejects the need for inductive confirmation altogether, arguing that scientific theories gain support through rigorous testing and potential refutation rather than probabilistic generalization, thereby sidestepping the problem by denying induction's central role in science.^[3] Bayesian approaches, drawing on probability theory, offer a framework where inductive updates occur via conditionalization on evidence, with prior beliefs revised to posteriors, though critics contend this merely formalizes the issue without resolving the justification of initial priors or the uniformity assumption.^[4] More recent material theories, such as John D. Norton's, dissolve the problem by rejecting universal rules of induction in favor of domain-specific factual postulates that license inferences locally, avoiding both circularity and regress.^[2] These debates underscore the problem's enduring influence on understandings of evidence, prediction, and rational belief formation across philosophy and science.

Historical Origins

Ancient Skepticism

The earliest skeptical challenges to inductive reasoning emerged in ancient Greek and Indian philosophical traditions, where thinkers questioned the reliability of generalizing from observed particulars to unobserved cases. In the Pyrrhonian school of skepticism, as articulated by Sextus Empiricus around 200 CE, induction was critiqued through what is known as the "mode of induction," highlighting the inability of past uniformities to guarantee future instances without falling into circularity or infinite regress. Sextus argued in his Outlines of Pyrrhonism that to justify an inductive generalization—such as expecting the sun to rise tomorrow based on prior observations—one must either assume the uniformity of nature indefinitely into the past (leading to regress, as each justification requires further evidence) or presuppose that past patterns will hold in the future, which begs the question by relying on the very inductive principle under scrutiny (PH I.166–177).^[5] This dilemma rendered induction dogmatic, as it could not be non-circularly validated, prompting the Pyrrhonist practice of epoché—suspension of judgment—to achieve mental tranquility by avoiding unsubstantiated beliefs.^[6] In parallel, ancient Indian philosophy featured skeptical challenges to induction from the Cārvāka (Materialist) school, which denied the possibility of establishing universal connections (vyāpti) from particular observations, arguing that unobserved counterinstances could always undermine generalizations—for instance, inferring that all fires are hot from observed flames risks failure if a cold fire exists beyond experience.^[7] The Nyāya Sūtras, composed around the 2nd century BCE by Akṣapāda Gautama, formalized anumāna (inference) as a means of knowledge involving the inference of universals from observed instances and absence of counterexamples, such as concluding that a hill has fire because it has smoke, based on established pervasion.^[8] Nyāya thinkers like Udayana (10th century CE) defended anumāna as yielding apodictic certainty when vyāpti is rigorously verified through repeated positive and negative observations and logical scrutiny (tarka), countering Cārvāka skepticism by emphasizing methods to exclude exceptions and ensure reliability.^[7]^[9] These ancient skeptical perspectives framed induction as presumptuous and unreliable, viewing reliance on it as a form of intellectual dogmatism that invites doubt and restraint in belief formation. By exposing the circularity and incompleteness of inductive justifications, Pyrrhonists and Cārvāka philosophers alike advocated suspending firm commitments to inductive conclusions, laying groundwork for later, more systematic inquiries into the problem.^[5]^[7]

Medieval and Early Modern Precursors

In the medieval period, inductive skepticism emerged within scholastic philosophy, particularly through the works of Nicholas of Autrecourt (c. 1299–after 1350), who challenged the inference of causal necessity from observed correlations. Autrecourt argued that repeated observations of events succeeding one another do not demonstrate a necessary connection between them, as such inferences rely on probabilistic assumptions rather than demonstrative certainty.^[10] He proposed occasionalism as a resolution, positing that God directly causes all events, rendering natural causation illusory and aligning empirical observations with divine omnipotence without committing to human-derived inductive necessities.^[11] This view attempted to reconcile theological determinism with skeptical doubts about causation, though it left unresolved the circularity of assuming divine regularity to justify observations.^[12] Transitioning to the early modern era, Francis Bacon (1561–1626) advanced induction as a cornerstone of scientific inquiry in his Novum Organum (1620), advocating a systematic ascent from particular observations to general axioms through controlled experiments and exclusion of biases.^[13] Yet Bacon implicitly acknowledged the limitations of even refined induction, critiquing simple enumeration—relying solely on positive instances—as "precarious and exposed to peril from a contradictory instance," which could undermine generalizations without exhaustive verification.^[14] His method sought to harmonize empirical progress with a providential worldview, portraying nature's laws as discoverable through induction while attributing ultimate order to God's design, thus bridging theological faith and nascent science without eliminating inductive vulnerabilities.^[15] John Locke (1632–1704) further developed empiricist foundations for induction in An Essay Concerning Human Understanding (1689), asserting that ideas of causation derive from sensory experience of repeated conjunctions between events, such as fire consistently heating objects.^[16] However, Locke maintained that these observations yield no perception of a "necessary connection" binding cause to effect, limiting causal knowledge to probable expectations rather than demonstrative truth.^[17] This empiricism endeavored to integrate inductive reasoning with Christian theology by grounding human understanding in God's created order, evident through experience, yet it perpetuated tensions between empirical reliability and the unverifiable necessity underlying generalizations.^[18]

Core Formulations

David Hume's Classic Problem

David Hume articulated the problem of induction in his seminal works A Treatise of Human Nature (1739–1740) and An Enquiry Concerning Human Understanding (1748), where he argued that inductive reasoning, which extrapolates general principles from specific observations, fundamentally relies on an unproven assumption about the uniformity of nature.^[19]^[1] Specifically, induction presupposes that "the future will be conformable to the past" or that unobserved instances will resemble those previously experienced, yet this principle cannot be established through reason or observation without begging the question.^[20] Hume emphasized that all experimental conclusions proceed upon this supposition, but no empirical evidence can justify it, as any such evidence would itself depend on inductive inference.^[21] The logical structure of Hume's argument highlights a deep circularity in attempts to justify induction. If one seeks to prove the uniformity principle through induction—by citing past uniformities to predict future ones—this merely assumes the very principle in question, rendering the justification circular.^[20] Alternatively, deductive reasoning, which Hume distinguished as concerning "relations of ideas" (such as mathematical truths that are intuitively or demonstratively certain), cannot apply here, as inductive inferences pertain to "matters of fact" about the world, where the contrary is always conceivable without contradiction.^[22] In causation, for instance, we observe constant conjunctions between events but have no rational basis for expecting their continuation, as the necessary connection arises not from the objects themselves but from our mental associations.^[23] This circularity leads to profound skeptical implications for knowledge justification, as reason alone provides no foundation for inductive beliefs. Instead, Hume contended that such beliefs stem from custom and habit, which incline the mind to associate ideas based on repeated experiences, forming the psychological basis for expecting uniformity without rational warrant.^[24] "Custom, then, is the great guide of human life," Hume wrote, underscoring that while this mechanism renders experience useful, it does not elevate induction to a justified form of knowledge.^[25] Thus, Hume's formulation challenges the epistemic status of scientific and everyday predictions, revealing induction as a non-rational propensity rather than a demonstrable method.^[26]

Nelson Goodman's New Riddle

In his 1955 book Fact, Fiction, and Forecast, philosopher Nelson Goodman introduced a reformulation of the problem of induction, shifting focus from the justification of inductive inferences to the selection of appropriate predicates for prediction.^[27] Goodman argued that traditional accounts, which assume evidence uniformly supports generalizations, fail to distinguish between projectible predicates—those that legitimately extend to unobserved cases—and non-projectible ones that lead to absurd predictions.^[27] This "new riddle" arises because the same evidence can confirm incompatible hypotheses depending on how predicates are defined, challenging the very basis of scientific forecasting.^[27] Central to Goodman's paradox is the predicate grue, defined as the property of an object being green if observed before a specific future time t (such as the year 2000) and blue otherwise.^[27] Suppose all emeralds examined before t are green; this evidence confirms both the hypothesis "all emeralds are green" and "all emeralds are grue," since the observed emeralds satisfy the grue condition by being green before t.^[27] Yet, after t, the first hypothesis predicts green emeralds, while the second predicts blue ones, rendering the grue hypothesis unprojectible despite equal evidential support.^[27] This demonstrates that induction does not merely rely on observed uniformity, as in Hume's classic problem, but requires criteria for which predicates are confirmable by their instances.^[27] An analogous example illustrates the issue with other observations: all crows observed to date are black, confirming "all crows are black" but also potentially "all crows are grackles," where grackles are black if observed before 2000 and white thereafter.^[28] Here, "black" is projectible due to its established role in natural laws, whereas "grackle" is not, as it introduces an arbitrary temporal cutoff that disrupts predictive consistency.^[28] Goodman's new riddle thus refines Hume's concern with temporal uniformity by emphasizing the qualitative problem of predicate choice: evidence alone cannot determine which generalizations are lawlike without additional rules.^[27] To resolve this, Goodman proposed that projectibility depends on the entrenchment of predicates, where a predicate is entrenched if it or similar terms have been successfully projected in past confirmed hypotheses, building a linguistic and historical foundation for induction.^[29] He also invoked similarity among instances, suggesting that projectible predicates group objects in ways that resemble established scientific categories rather than contrived ones like grue.^[29] However, these rules face criticism for their inductive basis: entrenchment relies on prior successful projections, risking circularity by presupposing the validity of the very inductions it seeks to justify.^[30] Despite this, Goodman's framework highlights the entrenched nature of everyday predicates like "green" or "black" in scientific practice, distinguishing them from ad hoc alternatives.^[30]

Philosophical Responses

Inductivist Defenses

David Hume, while formulating the problem of induction, offered a partial resolution by acknowledging that inductive inferences cannot be justified by reason but are instead rooted in custom or habit, an instinctive psychological mechanism that drives human belief formation despite its irrationality. This habit, Hume argued, is psychologically inevitable and practically indispensable, as it underpins expectations about the future uniformity of nature and supports survival by enabling reliable predictions based on past experiences. John Maynard Keynes, in his 1921 work A Treatise on Probability, sought to justify induction through a logical interpretation of probability, positing that inductive inferences can be rationally supported by degrees of partial belief calibrated to evidence, without requiring absolute certainty. Keynes emphasized weighted analogies and the limits of probability as tools for induction, arguing that such methods provide a non-circular evidential basis for generalizing from observed instances to unobserved ones, thereby rendering induction a defensible epistemic practice. Philosophers Keith Campbell and Claudio Costa have advocated a "biting the bullet" strategy, accepting basic inductive postulates as self-evident axioms necessary for rational inquiry, rather than demanding further justification. Campbell, in particular, contends that the apparent circularity in justifying induction via induction itself is non-vicious, as the reliability of inductive rules need not be presupposed in advance but emerges as inherent to the practice of empirical reasoning. Costa extends this by proposing a semantic framework where inductive principles are foundational to meaningful discourse about the world, treating them as primitive assumptions akin to logical axioms. David Stove defended induction against skeptical pessimism by arguing that the historical success of inductive methods provides non-circular evidence for their future reliability, as the track record of accurate predictions from past observations demonstrates a probabilistic alignment between samples and populations. In The Rationality of Induction (1986), Stove critiqued overly pessimistic views of induction's justification, asserting that the cumulative evidence from successful inductions outweighs hypothetical doomsday scenarios, thereby establishing induction as rationally preferable. Donald Williams, in The Ground of Induction (1947), portrayed induction as a fundamental logical relation grounded in probabilistic principles, prioritizing the "straight rule" of induction— which infers the proportion of a population from a representative sample—over inverse probability inferences.^[31] Williams maintained that this rule is logically sound because, in a combinatorial sense, observed frequencies reliably approximate overall distributions, providing a self-correcting mechanism that justifies inductive generalization without vicious circularity.^[31]

Falsificationist Critiques

Karl Popper, in his seminal work The Logic of Scientific Discovery originally published in 1934, fundamentally rejected the role of induction in scientific methodology, arguing instead that science progresses through the formulation of bold conjectures followed by rigorous attempts at refutation.^[32] He contended that inductive inference, which seeks to generalize from specific observations to universal laws, cannot provide a logical foundation for scientific knowledge, as it leads to an infinite regress or reliance on unproven assumptions.^[32] Popper's approach emphasized deductive testing, where hypotheses are subjected to empirical scrutiny not to confirm them but to potentially falsify them.^[33] Popper viewed the problem of induction—famously articulated by David Hume—as ultimately unsolvable, since no amount of confirmatory evidence can logically verify a universal theory, whereas a single contradictory instance can deductively refute it.^[32] In this framework, scientific theories are never proven true but gain temporary support through surviving severe tests aimed at falsification.^[33] This deductive asymmetry resolves the inductivist dilemma by eliminating the need for inductive justification altogether, rendering the traditional problem irrelevant to the logic of science.^[32] A key element of Popper's critique is his criterion of demarcation, which posits that falsifiability serves as the distinguishing feature between scientific theories and pseudoscientific or metaphysical claims, thereby circumventing the circularity inherent in inductivist justifications of science.^[33] Theories must be empirically testable in principle, meaning they make predictions that could be contradicted by observation, unlike non-falsifiable assertions that evade refutation.^[32] This standard avoids Hume's challenge by grounding scientific rationality in the potential for criticism and error elimination rather than uncritical acceptance of inductive patterns.^[33] Popper's falsificationism directly critiques verificationist approaches, which rely on accumulating positive instances to support generalizations. For instance, repeated observations of white swans do not confirm the universal statement "all swans are white," as such induction lacks logical force; however, the discovery of a single black swan definitively falsifies the hypothesis through strict deduction.^[32] This example underscores the one-sided nature of empirical testing: confirmation is illusory and psychological, while falsification is objective and logical.^[33] The broader implications of Popper's views portray induction as a methodological myth perpetuated by inductivists, with scientific corroboration being provisional and always open to future refutation based on the theory's resilience against attempted falsifications.^[32] Theories that withstand repeated severe tests earn higher degrees of corroboration, but this does not equate to inductive verification; instead, it reflects the critical growth of knowledge through trial and error.^[33] Thus, Popper's framework shifts the emphasis from seeking confirmatory evidence to designing experiments that maximize the risk of refutation, fostering scientific progress without reliance on problematic inductive logic.^[32]

Probabilistic and Bayesian Approaches

Probabilistic and Bayesian approaches to the problem of induction reframe the challenge by treating inductive inferences not as guarantees of certainty, but as rational updates to degrees of belief based on evidence, thereby providing a framework for measuring confirmation incrementally rather than all-or-nothing.^[34] In this view, induction becomes a process of belief revision where hypotheses are evaluated in terms of their probabilistic support from observed data, avoiding Hume's demand for deductive justification by embracing uncertainty as inherent to empirical reasoning.^[35] Central to Bayesianism is the application of Bayes' theorem, which formalizes how prior probabilities for hypotheses are updated with new evidence to yield posterior probabilities. The theorem states:

P(H|E) = \frac{P(E|H) \cdot P(H)}{P(E)}

where P(H) is the prior probability of hypothesis H, P(E|H) is the likelihood of evidence E given H, and P(E) is the marginal probability of E.^[36] This update rule allows induction to proceed as a coherent process of incrementally confirming or disconfirming hypotheses, with repeated applications leading to convergence on beliefs that reflect the true structure of the world under suitable conditions.^[37] Rudolf Carnap developed a foundational system of logical probability in the 1950s, aiming to construct an inductive logic as a formal calculus for assigning degrees of confirmation to hypotheses relative to evidence. In his framework, confirmation functions c(h, e) quantify how much evidence e supports hypothesis h, drawing on symmetric logical relations between linguistic structures to derive objective-like probabilities within a subjectivist envelope.^[36] Carnap's approach sought to systematize inductive support in a way that parallels deductive logic, providing a continuum of confirmation values from 0 to 1.^[38] Bayesian responses to Hume's problem treat the principle of uniformity of nature as an implicit prior assumption in the probability assignments, justified not a priori but pragmatically through the coherence of rational belief. Bruno de Finetti's 1937 theory of subjective probability argues that probabilities represent personal degrees of belief, which must satisfy the axioms of probability to avoid "Dutch book" arguments—situations where inconsistent beliefs lead to guaranteed losses in fair bets.^[39] This coherence condition ensures that inductive practices converge to truth in the long run, as agents with proper priors will asymptotically align their posteriors with objective frequencies via repeated Bayesian updates.^[34] Regarding Goodman's new riddle, Bayesian models address projectibility by incorporating similarity metrics or simplicity priors that favor hypotheses with predicates like "green" over contrived ones like "grue," assigning higher initial probabilities to more natural or entrenched concepts based on their explanatory power and predictive success.^[40] For instance, Elliott Sober's analysis shows that without a background model specifying relevant similarities, grue-like hypotheses lack the evidential support to compete with standard ones in Bayesian confirmation.^[40] Modern extensions of these ideas, such as those by Patrick Suppes, integrate Bayesian decision theory with inductive logic to handle concept formation and hypothesis selection under uncertainty, emphasizing how priors can be refined through empirical feedback in scientific contexts.^[41] Suppes' work builds on Carnap and de Finetti by exploring probabilistic models for empirical laws, where induction justifies beliefs via their utility in guiding actions and predictions.^[42]

Contemporary Implications

In Scientific Methodology

In scientific methodology, the problem of induction manifests prominently through underdetermination, where multiple competing theories can accommodate the same empirical data equally well, preventing inductive inference from uniquely selecting one theory over others. This issue is central to the Duhem-Quine thesis, which posits that scientific hypotheses are tested not in isolation but as part of holistic systems involving auxiliary assumptions, such that any observation confirming or disconfirming a prediction implicates the entire network rather than a single hypothesis.^[43] As articulated by Pierre Duhem in his analysis of physical theory, empirical evidence underdetermines theoretical choices because background assumptions about instruments, conditions, and interpretations can always be adjusted to preserve a favored hypothesis. Willard Van Orman Quine extended this holism to all empirical knowledge, arguing that no statement is empirically testable in isolation due to the interconnected web of beliefs facing the "tribunal of experience" as a collective. A key challenge to inductive confirmation in scientific practice is illustrated by the raven paradox, which highlights counterintuitive implications of standard logical accounts of hypothesis support. Formulated by Carl Hempel, the paradox arises from the hypothesis "all ravens are black," which, by logical equivalence, is confirmed not only by observing black ravens but also by non-black non-ravens, such as a white shoe; yet, intuitively, the latter observation seems irrelevant to the hypothesis. Hempel's analysis in the 1940s showed that this follows from the equivalence condition in confirmation theory, where evidence supporting a logically equivalent statement must support the original, straining the inductive intuition that confirmation should derive from relevant instances alone. This paradox underscores how inductive methods in hypothesis testing can lead to paradoxical outcomes, complicating the assessment of theoretical support in empirical sciences. Historical episodes, such as the development of quantum mechanics in the early 20th century, exemplify the limits of induction when anomalies disrupt uniform expectations derived from past observations. Classical physics, built on inductive generalizations from macroscopic phenomena, failed to predict phenomena like blackbody radiation and the photoelectric effect, where experimental results defied expectations of continuous energy distribution and wave-particle duality. These crises revealed induction's vulnerability to unforeseen regularities, as accumulating data did not incrementally refine theories but instead necessitated abandoning inductive projections from classical precedents, leading to revolutionary shifts rather than gradual confirmation. Despite these challenges, modern scientific methodology relies on induction through strategies like auxiliary hypotheses and systematic error mitigation to approximate causal inferences. John Stuart Mill's methods of agreement and difference, for instance, facilitate inductive reasoning by identifying common factors across instances (agreement) or isolating variables present only in outcomes (difference), thereby strengthening causal claims amid underdetermination. Scientists routinely employ such techniques, supplemented by auxiliary assumptions about experimental controls, to mitigate inductive fallibility and advance theory choice. However, induction remains essential yet inherently fallible in scientific practice, often culminating in paradigm shifts when accumulated anomalies render existing frameworks untenable, as Thomas Kuhn described in his account of scientific revolutions.

In Epistemology and AI

The problem of induction poses significant challenges in contemporary epistemology, particularly for theories of justified belief such as reliabilism, which posits that a belief is justified if it results from a reliable process that tends to produce true beliefs.^[44] Alvin Goldman's seminal 1979 formulation of process reliabilism emphasizes the reliability of belief-forming mechanisms, yet the inductive step—extrapolating from observed instances to unobserved ones—lacks a non-circular justification, as any appeal to past reliability itself relies on induction.^[44] This circularity undermines reliabilist accounts of inductive knowledge, raising skepticism about whether beliefs based on inductive inference can be deemed reliably truth-tracking without begging the question.^[45] In artificial intelligence, particularly machine learning, the problem of induction manifests as overfitting, where models trained on finite datasets capture noise or idiosyncratic patterns rather than generalizable truths, leading to poor performance on unseen data.^[46] This echoes Hume's classic concern, as algorithms induce hypotheses from training examples but cannot guarantee extrapolation without assuming the uniformity of nature. For instance, neural networks can exhibit biases akin to Nelson Goodman's "grue" paradox, where adversarial examples—subtle perturbations that fool models—reveal how inductive learning favors projectible predicates like "green" over contrived ones like "grue," yet still succumbs to hidden distributional shifts that invalidate generalizations. Contemporary debates in AI epistemology draw on Solomonoff induction, a formal approach from the 1960s that uses algorithmic complexity as a prior to select simpler hypotheses, approximating an ideal solution to the induction problem by assigning higher probabilities to shorter programs generating observed data.^[47] However, this method remains computationally intractable, as it requires enumerating all possible Turing machines, limiting its practical application in AI systems.^[48] The no-free-lunch theorems, introduced in the 1990s, underscore inherent limitations in learning from finite data, proving that no algorithm outperforms others on average across all possible distributions without inductive biases.^[49] Responses include ensemble methods, such as random forests that aggregate multiple models to mitigate overfitting, and regularization techniques like L2 penalties or dropout in neural networks, which impose simplicity constraints to enhance generalization.^[49] Emerging links to AI ethics highlight how unresolved inductive issues perpetuate societal harms; for example, inductive biases in predictive policing algorithms, trained on historical crime data, amplify racial disparities by overgeneralizing from biased samples, leading to discriminatory outcomes.^[50] Similarly, in climate modeling, machine learning models' reliance on inductive extrapolation from limited observational data can embed erroneous assumptions about future patterns, exacerbating uncertainties in policy decisions. These applications underscore the need for ethical frameworks that address inductive skepticism beyond technical fixes, ensuring AI systems do not entrench flawed generalizations.^[50]

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[PDF] Chapter 5 Reliabilism, Induction, and Scepticism - SAS-Space
In sections 5.9-13 I shall then show how this reliabilist account of knowledge provides an answer to the traditional sceptical problem of induction. Sections ...
[46]
[2401.09011] Inductive Models for Artificial Intelligence Systems are ...
Jan 17, 2024 · It highlights the `problem of induction' : the philosophical issue that past observations may not necessarily predict future events, a ...
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[PDF] Does Algorithmic Probability Solve the Problem of Induction?
We will begin with a definition of Algorithmic Probability (ALP), and discuss some of its properties. From these remarks it will become clear that it is ex-.
[48]
[2408.12065] Transformers As Approximations of Solomonoff Induction
Aug 22, 2024 · Abstract:Solomonoff Induction is an optimal-in-the-limit unbounded algorithm for sequence prediction, representing a Bayesian mixture of ...
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The No Free Lunch Theorem, Kolmogorov Complexity, and the Role ...
Apr 11, 2023 · No free lunch theorems for supervised learning state that no learner can solve all problems or that all learners achieve exactly the same accuracy on average.
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Artificial Intelligence, Predictive Policing, and Risk Assessment for ...
Nov 13, 2020 · There are widespread concerns about the use of artificial intelligence in law enforcement. Predictive policing and risk assessment are salient ...
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[PDF] AI Ethics in Predictive Policing - Peter Asaro's WWW
This article outlines a new ethical approach that bal- ances the promising benefits of AI with the realities of how information technologies and AI algorithms ...Missing: climate | Show results with:climate