Chandan Singh | philosophy

philosophy

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Notes on philosophy relevant to explanation (particularly in science)

basics

  • try to understand what principles underly all phenomena

interpretable ml

  • Machine Learning and the Future of Realism (hooker & hooker, 2017)
    • lack of interpretability in DNNs is part of what makes them powerful
    • naked predictions - numbers with no real interpretation
      • more central to science than modelling?
      • no theory needed? (Breiman 2001)
    • old school: realist studied neuroscience (Wundt), anti-realist just stimuli/response patterns (Skinner), now neither
    • interpretability properties
      • simplicity - too complex
      • risk - too complex
      • efficiency - basically generalizability
      • unification - answers ontology - the nature of being
      • realism in a partially accessible world
    • overall, they believe there is inherent value of ontological description
  • Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective (Emmert-Streib et al. 2020)

    • explainable AI is not a new field but has been already recognized and discussed for expert systems in the 1980s
      1. in some cases, such as simple physics, we can hope to get a theory - however, when the underlying process is complicated, interpretation can’t hope to simplify it
      2. in other cases, we might hope just for a description

scientific explanation (SEP)

  • distinction between scientific explanation and non-scientific explanation (although can put things on a spectrum)
  • distinction between explanations and accounts which are “merely descriptive”
  • these theories focus only on explanations of why things happen
  • concepts: explanation, description, causation

some overarching examples - what causes what?

  • do birth pills stop pregnancy (both for men/women)?
  • sun, flagpole, shadow
  • barometer, air pressure, storm
  • collision of pool balls
  • supply/demand curves in economics
  • gas law $PV=nRT$

DN model = Deductive-Nomological Model

  • by Popper, Hempel, Oppenheim (popper 1935, hempel 1942)
  • explanation has 2 constituents
    • explanandum - sentence describing phemonenon to be explained
    • explanans - class of sentences used to account for the phenomenon
  • deductive
    • explanans must be true
    • explanandum must be a logical consequence of the explanans
  • nomological = “lawful”
    • explanans must contain a “law of nature”
    • the law of nature must be essential to deriving the explanans
  • still hard to decide exactly what is a law - should be exceptionless generalizations describing regularities
    • can have probabilistic laws as well (e.g. prob of recovering after taking penicillin is high)
  • why this framework
    • by framing terms of laws and cricumstances, the explanation shows that the phenomenon was to be expected and helps us understand why it occured
    • sometimes, an explanation-sketch uses words like cause, that can be reframed more precisely in the DN model
  • counterexamples
    • assymetry (e.g. shadow length, flagpole height, sun angle) - derive shadow length seems explanatory, but not deriving flapole height
    • irrelevant details (e.g. being a man + taking birth control pills explains why you don’t get pregnant)
    • feels like their is something missing about “causality”, but this is difficult to pin down - suggests DN model states necessary but not sufficient conditions
    • features of an explanation must be recognized / used by users of an explanation

statistical relevance - wesley salmon

  • starts with (salmon, 1971)
  • given some class or population A, an attribute C will be statistically relevant to another attribute B iff $P(B∣A,C) \neq P(B∣A)$
    • find a set of attributes which divide the target into a homogenous partition = even if we split the cells further, they keep the same probability
      • like in causal inference, assume no missing variables
    • then, the explanation for a new target x gives the cells, the prob. for each of the cells, and which cell x belongs to
  • this method is almost information-theoretic - same explanans equally explains the same model with inverted probabilies
  • ex. (Salmon, 1971) atmospheric pressure, barometer, and storm - barometer is explanatory but not causal

causal mechanical models

  • starts with (salmon, 1984)
  • elements
    • causal process - leaves marks on the world which persist spatiotemporally (these marks hint at counterfactuals)
      • contrasts with a pseudoprocess, like a shadow
    • causal interaction - interaction of such processes, e.g. a car crash
  • explanation consists of 2 parts which both track causal process + interactions
    • etiological - leading up to event
    • constitutive parts - during event
  • issues: hard to distinguish between explanatory causes (e.g. for a pool ball collision, mass + velocity) vs other so-called causal processes (e.g. chalk mark on ball)
    • tends towards overly complex physical descriptions - e.g. for gas law track all particles rather than something global like pressure

unificationist models

  • important attempts include friedman (1974) and kitcher (1989)
  • seeks a unified account of a range of different phenomena
  • best explanations explains most phenomena with as few + as stringent arguments possible
  • potential issues
    • causal asymmetries - equally likely to say planets in future cause motion now then planets now cause motion in the future - this is honestly probably fine
    • doesn’t easily admit laws at different graunlarities

pragmatic = contextual explanation models

  • scriven (1962), bromberger (1966), van Fraassen (1980), achinstein (1983)
  • takes audience into account
  • others have been after characterizing a single “true” explanation and the role of the audience was minimized
  • “pragmatic” here means not just useful but also explicitly considering psychology + context
  • “Causal–explanatory pluralism” (lombrozo 2010 [PDF]) - subjects prefer explanations that appeal to relationships that are relatively stable (in the sense of continuing to hold across changing circumstances)
  • constructive empiricism (Bas van Fraasen)
    • just want theories that are “empirically adequate”
    • explanations are answers to questions - usually why? questions
      • explanations pick something from a set and tell why it is not any of the other things in the set (the set is given from context)
    • explanation is …“a t three-term relation between theory, fact, and context”
    • asymmetries coem of rom context
  • main criticism: this theory is too flexible, ironically it might be too flexible to be meaningfully (practically) useful
    • however, it is possible that this is the most flexible framework that is still accurate
    • want context to come in for as few steps as possible during an explanation - maybe we don’t need to analyze human psych
    • somewhat circular - if this is true, then how can we resolve ambiguities?

open areas

  • understand casuality
  • more focus on whethery expalanations capture our intuitive judgements and more on the issue of why the info they convey is valuable + relates to our goals
  • to what extent does a single model work across sciences (e.g. biologists claim to be interested in mechanisms whereas physicists in laws)

the story of philosophy

book by will durant

  • states his own view that philosophy should focus on ethics rather the epistemiology (i.e. how we know what we know)
    • every science begins as philosophy and ends as art
  • some definitions of philosophy
    • pursuit of fundamental laws
    • quest of unity
  • plato
    • socrates, plato’s teacher pursued stricter definitions and was put to death
    • plato writes the Republic - fictitional dialogue w/ socrates as the protagonist
      • argues that democracy failes because people are greedy
      • advocates for an absolute meritocracy with 3 classes
        • ruling class should live like communists, decent state salary, disallowed from excess
        • soldiers / auxiliaries
        • general population
      • requires equality of education
      • requires religion to placated the non-ruling majority
      • excess is regulated
      • justice = having and doing what is one’s own
        • each shall receive equivalent to what he produces + perform function for which he is best fit
      • juxtapositions
        • jesus: kindness to the weak
        • nietzsche: bravery of the strong
        • plato: effective harmony of the whole
    • only 3 things: truth, beauty, + justice
  • aristotle
    • starts systematic science, library science, and logic
    • advocates for uinversals as individuals (e.g. a man, not man like Plat argues for)
      • this is more grounded in reality
    • theology: God moves the work like force, but does little else
    • science: infinitesimal distinctions - boundaries between plant/animal categories are blurry
      • form: man, matter: child = possibility of form
    • politics: ideally a monarchy / aristocracy but more realistically would be constitutional gov. (people determine needs, leaders determine how to meet them)
      • restrictions on pop.
      • believes in slavery / female inferiority
  • francis bacon
    • lived in 1500s/1600s in England
    • father of the scientific method
      • objective and realistic
      • in contrast to descartes = subjctive/idealistic
        • “I think therefore I am”
    • bacon embraces epicureanism - don’t want anything
    • scors knowledge that doesn’t lead to action
    • science = organization of knowledge
    • philosophy = organization of science
    • doubt all assumptions
  • spinoza
    • baroch de espinoza
    • jew who was excommunicated for anti-religious writing
    • no distinction between body and mind
    • no free will - only desires that guide everything
      • beginnings of doubting rationalism
  • voltaire
    • frenchman who was exiled
    • seeks history of ideas, beginning with The Essay on Morals
    • Candide - short story, denouncing optimism for pragmatism
    • real philosophy begins with Philosophic dictionary
    • strongly against superstition
    • wrote simple, accessible pamphlets
    • in his later years, turns to focus on the pursuit of usefulness rather than truth
    • contrasts with younger Roussea, who wanted more action, instinct, social contract
  • kant: mind has prior beliefes
    • what makes a math law better than some other thing? kant says a priori beliefs…interestingly those beliefs were from evolution in the first place
    • mind is not blank slate: mind filters in what we perceive a priori in contrast to growing popular belief that everything comes from perception
    • understanding can never go beyond the limits of sensibility
      • certain things in science/religion etc. can never be known, just interpreted
      • time and space are not realities but just our interpretations
    • lots of connections to priors in modern AI research
    • morals come from an innate sense
    • somewhat pro-religion but not fully, still still faced persecution in Prussia
    • “Have strongly-held values, and malleable opinions”. - Francois Chollet tweet
  • schopenhauer
    • everything is will: continuing trend from espinoza + kant against rationalism
    • pessimist: even in Utopia, ennui sets in
    • objects of science is universal that contains many particulars while object of art is particular than contains a universal – this requires more genius
  • herbert spencer
    • evolution as a guiding philosophy of everything
    • darwin published Origin of Species in 1859, when spencer was 40
      • spencer is thus more lamarckian
    • greatest contributions were to sociology: carefully curates data for sociology analysis
    • resulting philosophy is conservative, laissez-fare, anti-regulation
  • friedrich nietzsche
    • evolution as morality: favors the strong
    • germans have 2 words for good / bad - one is closer to strong, the other to kind
    • everything is due to an underlying will for power
    • evolution towards “the superman”
  • bertrand russel
    • starts with symbolic reasoning
    • after WWI, shifts tow grounded philosophy in pacifism, communism

stability

  • Foundationalism - where the chain of justifications eventually relies on basic beliefs or axioms that are left unproven
    • Plato’s Republic
  • the stability of belief: how rational belief coheres with probability (leitgeb, 2017) - introduction
  • To Explain or to Predict? (Shmueli, 2010)
    • explanatory modeling as the use of statistical models for testing causal explanations
    • many philosophies view explanation and prediction as distinct (but not incompatible)