Chandan Singh | philosophy

philosophy

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Notes on philosophy relevant to explanation (particularly in science)

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)

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
  • https://projecteuclid.org/download/pdfview_1/euclid.ss/1294167961

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

effective altruism

  • effectivealtruism
    • promising causes
      • Great in scale (it affects many lives, by a great amount)
      • Highly neglected (few other people are working on addressing the problem), and
      • Highly solvable or tractable (additional resources will do a great deal to address it).
    • 3 big areas
      • fighting extreme poverty (e.g. malaria)
      • animal suffering
      • improving the long-term future
  • rethink priorities jobs
  • open philanthropy
  • careers can have greater impacts than altruism (80k hours)
  • givewell cost-effective calculations
    • charities often exaggerate their “cost to save a life”