How Risk and Corporate Ethics Collide. Part I — An Argument for Increasing Corporate Risk

Hi all! This is my first attempt at an academic-style blog. I will be exploring in 3 posts my case for why increasing fiscal and criminal risk for corporations might serve to impose limits on the callous extremes they might go to when attempting to secure profit. In Part I (this part!), ‘An Argument for Increasing Corporate Risk’, I will lay out the three premises of my argument in summary and then in detail. In Parts II and III, ‘The Case of Liebeck v. McDonald’s’ and ‘The Case of the Ford Pinto’ respectively, I will describe two examples which reinforce the argument in Part I. This part will be a bit longer (1400 words) than the latter two (800–900 words each). Thank you for reading!

The American legal system often utilizes the notion of foreseeability to determine the proximate cause of harm in cases of negligence. If you were capable of reasonably anticipating the occurrence of harm when you chose to act in a certain way and that harm did in fact occur, then the law can find you culpable for that harm. This ability to anticipate harm by your actions evolves and becomes magnified as society becomes more statistical, thereby expanding the realm of foreseeability and increasing that which we may be held liable for, if we, like the American legal system, accept that foreseeability is a sufficient condition for negligence.

To restate this argument in an enumerated form:

(1) If injuries or damages occur because of an action and the actor could have reasonably foreseen those injuries or damages happening because of that action, then they ought to be held accountable for those consequences.

(2) Statistical analysis concerning human behavior expands the boundaries of reasonable foreseeability by informing us of the likelihood of consequences to those behaviors in terms of probability.

(3) Society endorses this quantitative worldview, thus legitimizing the increased foreseeability of actions made possible by social statistics.

[C] Therefore, those who act without regard to the statistically obvious chance that their actions could result in injury or damage should be held legally liable for the consequences of their actions.

I will argue that a heightened reliance on statistics in the courtroom can increase the responsibility of actors and can be used as a determinant of liability in lawsuits, as demonstrated in Liebeck v. McDonald’s, then recommend our society increases the risk to engagement in corporate crime (oft motivated by a perversion of similar statistical ideas) by reviewing the deleterious human effect of the Ford Pinto scandal using a deterrence theory framework.

The first premise of this argument is simply a restatement of the notion of foreseeability as a proximate cause for harm. Perhaps then it is important to consider why anyone should subscribe to the legitimacy of the ideas of foreseeability and proximate cause. The other major type of causation in law is known as cause-in-fact or the but-for condition, which states that an action is causal of an outcome if and only if the action was a necessary condition for the occurrence of that outcome. While this notion of but-for causation works without fault in many cases, one can easily imagine instances where it fails to discern who ought to receive blame.

For instance, suppose that two individuals unbeknownst to one another toss their lit cigarettes into dry grass in front of a house at the exact same time, resulting in a fire that burns down the house. Either one of the cigarettes being tossed into the dry grass would have been sufficient for starting the fire that burned down the house, but because both cigarettes started the fire, it seems that neither of the individuals necessarily caused the house to burn down. Because of this, it seems like neither of the individuals is liable for the house burning down under the logic of the but-for condition. This is where proximate cause becomes a useful concept, and with it the notion of foreseeability. By examining sufficient or proximate causes instead of only but-for causation, the law can place blame in situations where but-for causation fails to apply. In cases where the causal chains that lead to an unpleasant or unlawful result are tangled and confusing, we can use this notion of proximate causation as well, rendering this concept exceptionally useful for cases of liability where there seem to be a chain of actors that could or could not be somewhat or altogether responsible for the ultimate outcome.

The second premise states that statistics are capable of informing individuals and society of the risk tied to their actions and that in doing so they expand upon what ought to be considered reasonably foreseeable. Like other notions of causation, the concept of foreseeability as proximate cause is potentially infinite when applied without limitation. In fact, one could argue that virtually everything is foreseeable in the sense that a reasonable person can anticipate realities where different things occur. If I am trying to decide between taking one job or the other, I can experience in my mind disparate potential futures should I choose one job or the other. That capacity to imagine and foresee the consequences of our decisions and actions is the root to my claim that foreseeability is practically infinite. However, the idea that we should be held accountable for the outcomes of all possible scenarios is absurd. How then are we to determine what you should be held liable for?

This is where we receive the modification of reason to foreseeability to combat frivolity. However, the concept of reasonable foreseeability is still vague and generally unhelpful in assisting efforts to determine liability and negligence. This is perhaps intentional and left in such a way that juries, judges, the defense, and the prosecution can deliberate upon to maintain choice and initial indeterminacy of guilt in the courtroom. However, it is nonetheless still important for there to be methods and tools capable of eliminating some or all ambiguity of foreseeability. Statistics can play this disambiguating role such that if there is statistical evidence of a harmful outcome that is willfully ignored by the actor, then they ought to be held liable for their actions.

In the case of Liebeck v. McDonald’s, the defendant failed to provide adequate reasons for ignoring hundreds of cases where injuries similar to the plaintiff’s injuries were incurred from the spilling of their hot drinks and in failing to acknowledge the statistical likelihood of harm resulting from their actions proved to the court that they were in fact liable for Stella Liebeck’s injuries. Oftentimes, there is an insufficient amount of evidence to show liability or causation. Statistical analysis can serve as a significant type of evidence for proximate causation that assists the courts in determining liability in those difficult cases.

The third premise may seem contentious until you consider the wide-reaching effects of statistics on society in the twentieth and twenty-first centuries. Discussing this phenomenon of the quantified society, philosopher Ian Hacking states that “probability and statistics crowd upon us. The statistics of our pleasures and our vices are relentlessly tabulated… Our public fears are endlessly debated in terms of probabilities…” (Hacking 4). Society’s acceptance of the world as statistical is something we experience on an extremely deep and ingrained level. Hacking believes that statistics has pervasively shifted our way of understanding the world logically, metaphysically, epistemologically, and ethically. In the realm of ethics, probability does not alter the beliefs and values we are inclined to hold but instead it “lies at the basis of all reasonable choice made by officials… by covering opinion with a veneer of objectivity, we replace judgement by computation” (Hacking 4).

Understanding how deeply ingrained statistics and probability has become in our everyday lives, it follows that reasoning based on chance and other forms of statistical understanding is held as legitimate by both society and its laws. This legitimization makes actionable the increased foreseeability crafted by behavioral statistics and other mathematical understandings of human nature and it can be logically derived from this and the other premises that blame found through the statistical means of application of foreseeability and harm is legally valid and warrants proximate cause for negligent behavior.

Works Cited (in Parts I, II, and III):

Brown, S., Esbensen, F., Geis, G., (2019). Criminology, Explaining Crime and its Context (Tenth Edition). Routledge.

Cain, Kevin G. “And Now, the Rest of the Story… The McDonald’s Coffee Lawsuit.” Journal of Consumer and Commercial Law, vol. 11, no. 1, 2007, pp. 14–19.

Davies, J., and Ollus, N. (2019). “Labour exploitation as corporate crime and harm: outsourcing responsibility in food production and cleaning services supply chains”. Crime, Law, and Social Change.

Griffin III, O., and Spillane, J. (2016). “Confounding the process: forgotten actors and factors in the state-corporate crime paradigm”. Crime, Law, and Social Change.

Hacking, Ian. (2010). The Taming of Chance. Cambridge University Press.

Shaw, W., and Barry, V. (2001). Moral Issues in Business (Eighth Edition).