Chandan Singh | philosophy

philosophy

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

# 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”