Science and values. A two-way relations
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Science and values. A two-way relations
- 1. SCIENCE AND VALUES: A TWO-WAY RELATION Emanuele Ratti and Federica Russo @em_rattiphilsci | @federicarusso 1
- 2. Outline ■ The classic debate ‘Science and Values’ ■ Directions of influence: ■ From values to science, from science to values ■ Concepts and methods can promote values ■ Two case studies: ■ Conceptualizing health and disease ■ Risk assessment tools in the justice system 2
- 3. THE CLASSIC DEBATE ON SCIENCE AND VALUES 3
- 4. Debunking the value-free ideal ■ A diehard thesis in some Phil Sci ■ A long tradition of exploring the role of values in the practice of science ■ Choices are value-laden, and at different stages of the process ■ There is an ‘irreducible’ inductive risk that we need to handle, not to eliminate 4
- 6. Ladenness goes in 2 directions: 1)Values influence our concepts/methods 2)Values are influenced by concepts and methods 1) is much more studied than 2) Value-ladenness 2.0 6
- 7. Value-promoting concepts and methods ■ The concepts and methods we use in the practice of science (and technology): ■ are at least compatible with some values or others ■ can promote some values or others ■ There is a normative dimension of science and technology that needs to be explored 7
- 8. A general argument IF we conceptualise / model X such-and-such THEN what actions should follow? ■ Replace X by your favourite: health, evidence, probability, … ■ Normativity is double – PhilSci concepts are non-neutral – PhilSci and Ethics are part and parcel of science/policy, not a cherry on the cake 8
- 10. The normativity of ‘health’ ■ ‘Health’ is certainly normative in the practice of public health – See rich literature in public health ethics ■ But ‘health’ is also normative at the level of the scientific concepts & methods – Whether social factors are proximate – rather than distant – causes makes a difference to which actions we decide to undertake 10
- 11. Bio-social causes of health and disease ■ Historically, 19th century public health is much about social factors ■ Recently, characterised as ‘the causes of causes’ ■ Sociology of health / social epidemiology – Health&disease are associated with social factors – Inequalities in health are associated with inequalities at the social level – Health&disease happen in a social context 11
- 12. A specific argument IF the social has active causal role in health&disease THEN what public health interventions should follow? 12
- 13. Social causes, biological interventions ■ Obesity ‘epidemic’ ■ Wide recognition of social factors (besides biological ones) ■ Top priority for EU health policy ■ EU announces to tackle social factors (e.g. behaviour) ■ One of the biggest actions: regulating food labelling ■ Ultimately tackles the biology of obesity ■ Claims to target food industry, but in fact it makes info available and leaves the choice to the individual person ■ Pulls in opposite directions with actions to improve on competitiveness of SMEs 13
- 14. What if social factors are proximate causes? ■ What to do with ■ Food labelling? ■ Food industry? ■ Marketing? ■ What consequences to draw from a concept that would (naturally?) lead to paternalist attitudes? ■ How to reconcile it with (justified?) libertarian intuitions? ■ Is a ‘libertarian paternalism’ a viable option? ■ … 14
- 15. RISK ASSESSMENT TOOLS IN THE JUSTICE SYSTEM 15
- 16. 16 • Widespread use of risk-assessment tools in the justice system (more than a dozen) • Example: Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) • COMPAS uses an algorithm to calculate a score of probability of recidivism • Defendants fill a questionnaire of 137 questions which is processed by a machine learning (ML) algorithm • The output is a risk score which is ‘predictive’ of recidivism • 1 to 4: low; 5 to 7: medium; 8 to 10: high
- 17. The specific argument GIVEN the specific characteristics of ML tools, IF such tools are used in the justice system THEN what notion of punishment and justice will be promoted in that system? 17
- 18. 18 • Biddle (2020): analysis on value-laden dimension of COMPAS in terms of inductive/epistemic risk • At each step of the ML pipeline, more than one decision is possible • Choices come with risk of errors • Value-judgement: which are the acceptable risks? Problem Identification and Framing Data Decisions and Model Competencies Algorithm Design: Accuracy and Explainability Algorithm Design: Conceptions of Fairness Algorithm Design: Outputs Deployment: Transparency and Opacity What is the composition of the baseline population? Which features potentially correlate with recidivism? Designers must make “contingent decisions that the possible system outputs would binary (…) and that the probability threshold would be what it is” (p 9) What concept of recidivism? What types of crime?
- 19. 19 • Pruss’ analysis (2021) is a step beyond inductive/epistemic risk • ML tools presupposes a formalist interpretation of legal principles, and blur the line between liability assessment and sentencing • Because of the emphasis on predictive features, ML tools presupposes a consequentialist notion of punishment • Is this value-promoting science? • Not completely, as in Pruss’ view choosing the ‘right’ variables in the design phase can still shape the moral dimension of ML tools • Our claim is that it is the very idea of using such tools that comes with a commitment to consequentialism, independently of the variables taken into account in the design phase
- 20. 20 Goal of punishment 1: “to assign a morally just punishment in proportion to what the defendant deserve” Goal of punishment 2: “to protect the public from the defendant’s future crimes” • The ‘values-to-science‘ position argue that data scientists have discretion in choosing which goal to promote, by committing to certain tradeoffs • The ‘science-to-values‘ position shows that values enter the picture in other subtle ways
- 21. 21 • Thesis: ML tools reinforce consequentialist views of punishment (CVP), independently of the value-laden choices made by data scientists • The goal of CVP is to limit the exposure of the public to criminal activities • One way to promote CVP is to know in advance who will be a danger to the public • ML, in principle, make this kind of prediction • ML and CVP support each other • You reinforce CVP because the goals that ML can possibly achieve in the penal system are CVP’s goals
- 22. TO SUM UP AND CONCLUDE 22
- 23. Division of labour? •Health Sci / Epi / ML / … >> methods •Phil Sci/ Health / Law / … >> concepts •Ethics-PolPhil >> norms But these questions are in fact tangled! 23
- 24. Phil Sci and Ethics-Political Phil ■ How to connect epistemological/methodological questions to normative questions? ■ Which values are promoted by our (non-neutral) sci-concepts? – Setting the stage for more science-informed ethics or ethics- aware philsci 24
- 25. SCIENCE AND VALUES: A TWO-WAY RELATION Emanuele Ratti and Federica Russo @em_rattiphilsci | @federicarusso 25 Thanks for your attention
Editor's Notes
- In the past decade, ML algorithms have been increasingly used in the American penal system. In addition to non-ML logistic regressions models, ML algorithms are widespread in the penal systems and they are used for a wide variety of tasks: bail, probation, length of a sentence. As Dasha Pruss has documented, algorithms for predicting recidivism have been promoted within the context of ‘evidence-based sentencing’, in the effort to reduce judges’ biases. Some have said that their ‘algorithmic’, ‘formalized’, and ‘quantitative’ nature, make these tools ‘value-free’. This is the typical setting for the inductive risk approach: let’s show that they are not value-free [CLICK] One algorithm used for recidivism scores – COMPAS [CLICK] – has been the target of a journalistic investigation by ProPublica.[CLICK] COMPAS is a 137 questions form, which answers are then processed by a ML algorithm, which then generate a risk score. [CLICK] This is an example of typical questions you may find in that questionnaire. [CLICK] Risk scores are assigned to individuals on the basis of individual features that, by means of algorithmic procedures, are found to be correlated with a certain outcome in a particular (population) sample. [CLICK] The risk score per se is a just a number, but depending on certain numerical thresholds one can be ‘high risk’ or ‘low risk’. In deciding prison sentences, parole, bail amount, etc these numerical thresholds play an important role
- As I just said, claims that these algorithms are ‘value-free’ are the perfect hook for inductive risk scholars. This is what Biddle does in a comprehensive article published in the Canadian Journal of Philosophy. [CLICK] What Biddle shows is that at each step of the ML pipeline [CLICK], more than one technical choice is possible, [CLICK] but all options available come with risk of making a mistake. [CLICK] Because of this, a value-judgement (in the form of tradeoff) is necessary to justify which risk is acceptable and which not. Let me briefly show 3 examples. [CLICK] First, defining recidivism requires decisions about what count as a crime (re-arrest and conviction; re-arrest and being formally charged) and what kind of crimes (misdemeanor; felony). It involves also decisions on what count as a ‘triggering’ event. These decisions shift the threshold between who is counted as recidivist considerably. [CLICK] Another example is collecting data: how do you create the baseline population? Which are the features that should correlate with crimes? Criminal history? Age at first arrest? Family criminality? [CLICK] Finally, when outputs are concerned, Biddle says that how to translate a quantitative risk score into a qualitative risk output involves tradeoff decisions: what are the cutoffs for the different categories of risk?
- Another interesting article is the one written by Sasha Pruss. Pruss’ analysis is interesting, because she seems to describe the ‘science-to-values’ direction. In this paper she makes various claims. [CLICK] First, she makes the convincing case that the use of ML tools presupposes a formalist interpretation of legal principles, which is a ‘mechanical’ way of thinking about laws interpretation. Second, she shows that risk assessment algorithms blurs the line between liability assessment and sentencing. In the American context, liability assessment refers to the choice of a verdict (i.e. guilty or not-guilty), and it is usually done by juries, while sentencing is usually the domain of judges. [CLICK] In fleshing out this second thesis, she notices that the use of risk assessment algorithms like COMPAS, that concentrates on predictive features, presupposes that the purpose of punishment is consequentialist (crime control) rather than deontological. [CLICK] This looks like our claim: methods are morally charged, independently of the values-tradeoff you do. [CLICK] However, she also adds that the ‘consequentialist’ turn happens when algorithmic tools take into account ‘morally insignificant’ variables (such as demographic information, familial relationships, etc), and this seems to suggest - though she does not say this explicitly - that if we change these variables, then we would be able to orient the tool towards a deontological direction. This move would be compatible with the inductive risk perspective: we get to choose values by means of trade offs, and by carefully selecting the variables, our concerns about promoting the wrong values would dissolve. [CLICK] Our claim is that, independently of the variables used, ML tools promote a consequentialist notion of punishment if used in the penal system, and that’s because of the characteristics of these methods.
- In order to formulate our thesis, we start from these considerations made by Sonja Starr in this short article. Starr argues that in deciding punishment, our conception of what the purpose of sentencing is plays an important role. The goal of sentencing, continues Starr, is either (1) to assign a morally just punishment, or (2) to defend the public from defendant’s future crimes. [CLICK]Pruss and Biddle – as exemplifying the values-to-science position - may say that it pertains to the data scientist to choose which one to promote, by committing to certain tradeoffs or by choosing factors with more or less moral significance. This is certainly correct, [CLICK] but we think that values do enter in the picture also in another way, which is more subtle.
- This is our thesis, and this is how it works. [CLICK] We know that the goal of CVP is to limit the occurrences in which the public will be exposed to criminal activities. [CLICK] Intuitively, one way to promote CVP is to have a ‘crystal ball’ that will predict whether a person is likely to be a danger to the public. [CLICK] But this is exactly what ML tools can, in principle, deliver: the purpose of (most) ML tools is to predict or classify on the basis of past occurrences. The more these predictions or classifications are accurate, the more the tools fulfill their purpose: a virtuous ML tool is one that predicts well, independently of the variables it takes into account to predict. [CLICK] Therefore, CVP’s and ML tools’ goals are compatible: one way to achieve CVP is to predict who is going to commit crimes, and such prediction is [CLICK] exactly what ML provides - by definition - in the penal context. ML tools and consequentialist conceptions shake hands and support each other. The take-home message is that the moment in which you implement ML tools in the penal context, you are also endorsing CVP. The methodology of ML promotes a certain conception of justice in virtue of the fact that its goals and the goals underpinning that conception of justice are compatible. Better: the goal of CVP is just an instance of the goals that ML tools are designed to achieve by definition. If you promote the use of ML tools, then you also promote CVP.