Objectivity

Objectivity (#5): Three Reactions to the Challenge of Objectivity (in inference):

(1) If discretionary judgments are thought to introduce subjectivity in inference, a classic strategy thought to achieve objectivity is to extricate such choices, replacing them with purely formal a priori computations or agreed-upon conventions (see March 14).  If leeway for discretion introduces subjectivity, then cutting off discretion must yield objectivity!  Or so some argue. Such strategies may be found, to varying degrees, across the different approaches to statistical inference.

The inductive logics of the type developed by Carnap promised to be an objective guide for measuring degrees of confirmation in hypotheses, despite much-discussed problems, paradoxes, and conflicting choices of confirmation logics.  In Carnapian inductive logics, initial assignments of probability are based on a choice of language and on intuitive, logical principles. The consequent logical probabilities can then be updated (given the statements of evidence) with Bayes’s Theorem. The fact that the resulting degrees of confirmation are at the same time analytical and a priori—giving them an air of objectivity–reveals the central weakness of such confirmation theories as “guides for life”, e.g., —as guides, say, for empirical frequencies or for finding things out in the real world. Something very similar  happens with the varieties of “objective’” Bayesian accounts, both in statistics and in formal Bayesian epistemology in philosophy (a topic to which I will return; if interested, see my RMM contribution).

A related way of trying to remove latitude for discretion might be to define objectivity in terms of the consensus of a specified group, perhaps of experts, or of agents with “diverse” backgrounds. Once again, such a convention may enable agreement yet fail to have the desired link-up with the real world.  It would be necessary to show why consensus reached by the particular choice of group (another area for discretion) achieves the learning goals of interest.

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Objectivity (#4) and the “Argument From Discretion”

We constantly hear that procedures of inference are inescapably subjective because of the latitude of human judgment as it bears on the collection, modeling, and interpretation of data. But this is seriously equivocal: Being the product of a human subject is hardly the same as being subjective, at least not in the sense we are speaking of—that is, as a threat to objective knowledge. Are all these arguments about the allegedly inevitable subjectivity of statistical methodology rooted in equivocations? I argue that they are!

Insofar as humans conduct science and draw inferences, it is obvious that human judgments and human measurements are involved. True enough, but too trivial an observation to help us distinguish among the different ways judgments should enter, and how, nevertheless, to avoid introducing bias and unwarranted inferences. The issue is not that a human is doing the measuring, but whether we can reliably use the thing being measured to find out about the world.

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Objectivity #3: Clean(er) Hands With Metastatistics

I claim that all but the first of the “dirty hands” argument’s five premises are flawed. Even the first premise too directly identifies a policy decision with a statistical report. But the key flaws begin with premise 2. Although risk policies may be based on a statistical report of evidence, it does not follow that the considerations suitable for judging risk policies are the ones suitable for judging the statistical report. They are not. The latter, of course, should not be reduced to some kind of unthinking accept/reject report. If responsible, it must clearly and completely report the nature and extent of (risk-related) effects that are and are not indicated by the data, making plain how the methodological choices made in the generation, modeling, and interpreting of data raise or lower the chances of finding evidence of specific risks. These choices may be called risk assessment policy (RAP) choices. Continue reading

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Objectivity #2: The “Dirty Hands” Argument for Ethics in Evidence

Some argue that generating and interpreting data for purposes of risk assessment invariably introduces ethical (and other value) considerations that might not only go beyond, but might even conflict with, the “accepted canons of objective scientific reporting.”  This thesis, we may call it the thesis of ethics in evidence and inference, some think, shows that an ethical interpretation of evidence may warrant violating canons of scientific objectivity, and even that a scientist must choose between norms of morality and objectivity.

The reasoning is that since the scientists’ hands must invariably get “dirty” with policy and other values, they should opt for interpreting evidence in a way that promotes ethically sound values, or maximizes public benefit (in some sense).

I call this the “dirty hands” argument, alluding to a term used by philosopher Carl Cranor (1994).1

I cannot say how far its proponents would endorse taking the argument.2 However, it seems that if this thesis is accepted, it may be possible to regard as “unethical” the objective reporting of scientific uncertainties in evidence.  This consequence is worrisome: in fact, it would conflict with the generally accepted imperative for an ethical interpretation of scientific evidence.

Nevertheless, the “dirty hands” argument as advanced has apparently plausible premises, one or more of which would need to be denied to avoid the conclusion which otherwise follows deductively. It goes roughly as follows:

  1. Whether observed data are taken as evidence of a risk depends on a methodological decision as to when to reject the null hypothesis of no risk  H0 (and infer the data are evidence of a risk).
  2. Thus interpreting data to feed into policy decisions with potentially serious risks to the public, the scientist is actually engaged in matters of policy (what is generally framed as an issue of evidence and science, is actually an issue of policy values, ethics, and politics).
  3.  The public funds scientific research and the scientist should be responsible for promoting the public good, so scientists should interpret risk evidence so as to maximize public benefit.
  4. Therefore, a responsible (ethical) interpretation of scientific data on risks is one that maximizes public benefit–and one that does not do so is irresponsible or unethical.
  5. Public benefit is maximized by minimizing the chance of failing to find a risk.  This leads to the conclusion in 6:
  6. CONCLUSION: In situations of risk assessment the ethical interpreter of evidence will maximize the chance of inferring there is a risk–even if this means inferring a risk when there is none with high probability (or at least a probability much higher than is normally countenanced)

The argument about ethics in evidence is often put in terms of balancing type 1 and 2 errors.

Type I error:test T finds evidence of an increased risk ( H0 is rejected), when in fact the risk is absent (false positive)

Type II error:
test T does not find evidence of an increased risk ( H0 is accepted), when in fact an increased risk δ is present (false negative).

The traditional balance of type I and type II error probabilities, wherein type I errors are minimized, some argue, is unethical. Rather than minimize type I errors, it might be  claimed, an “ethical” tester should minimize type II errors.

I claim that at least 3 of the premises, while plausible-sounding, are false.  What do you think?
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(1) Cranor (to my knowledge) was among the first to articulate the argument in philosophy, in relation to statistical significance tests (it is echoed by more recent philosophers of evidence based policy):

Scientists should adopt more health protective evidentiary standards, even when they are not consistent with the most demanding inferential standards of the field.  That is, scientists may be forced to choose between the evidentiary ideals of their fields and the moral value of protecting the public from exposure to toxins, frequently they cannot realize both (Cranor 1994, pp. 169-70).

Kristin Shrader-Frechette has advanced analogous arguments in numerous risk research contexts.

(2) I should note that Cranor is aware that properly scrutinizing statistical tests can advance matters here.

Cranor, C. (1994), “Public Health Research and Uncertainty”, in K. Shrader-Frechette, Ethics of Sciencetific Research.  Rowman and Littlefield, pp. 169-186.

Shrader-Frechette, K. (1994), Ethics of Scientific Research, Rowman and Littlefield

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