A “cognitive bias” is a systematic pattern of deviation from rationality in judgment, as a result of which inferences about other people and situations are be drawn in an illogical fashion. This leads to individuals creating their own subjective reality, which they believe is the objective truth. Any reliance on a person’s belief, therefore, must be tempered with the possibility (or probability) that one or more cognitive biases are influencing that individual’s judgment. As a result judges and legal systems must be alive to them, and compensate for them (and of course its mitigation or elimination is the purpose of the “Scientific Method”).
Common sources of cognitive bias
- Anchoring. This involves people being over-reliant on the first piece of information that they receive. For example, if deciding how much money to award if initially given the sum of £10,000 the average amount awarded is likely to be higher than if the initial sum requested was £3,000.
- Availability heuristic. People overestimate the importance of failing to recognise your information that is available to them. We judge the probability of events by how quickly and easily examples come to mind. For example, are own cognitive biases or people with mental illness more likely to be the perpetrators or victims considering yourself less of violence? Because of media stories we are more likely to initially believe the former when in fact the latter is true.
- Bandwagon or herd effect. Adopting a belief increases based on the number of people who hold the belief. If other members of the tribunal speak first and are in agreement, the last person to speak may just go along with the view already expressed to fit in or look like they know what they are doing.
- Blind-spot bias. Failing to recognise your own cognitive biases or considering yourself less biased than others is a bias in itself. For example, I have an appraisal which says I ask inappropriate questions, I believe it to be wrong preferring to focus on the fact that I’ve never received a complaint about my questioning.
- Choice supportive bias. When you choose something you tend to feel positive about it even if that choice has flaws and you remember your choice as better than it actually was. For example, when a decision you have been involved in is overturned on appeal you are sure that the appeal body has not taken into account all of the factors you did and so your decision was the better one.
- Clustering illusion. This is the tendency to see patterns in random events. For example, fluctuations in the stock market price of shares where we ignore differences in data but stress similarities. This is a generalized version of the “gambler’s fallacy”, in which outcomes in random sequences should exhibit systematic reversals. When observing flips of a fair coin, for example, most people believe that a streak of heads makes it more likely that the next flip will be a tail.
- The “law of small numbers” or “local representativeness”. The tendency to believe that a small sample should resemble closely the underlying population, and hence believe that heads and tails should balance even in small samples. An interesting variation of this is the belief in “hot hands”, where for example a basketball player is more likely than average to make the next shot when on a “hot streak” as opposed to a temporary deviation from the mean performance of that player.
- Conservatism bias. Where people favour prior evidence over new evidence or information that has emerged. Similar to cognitive dissonance whereby new evidence is discounted or discredited this minimises new evidence in favour of our pre-existing way of looking at the world.
- Information bias. The tendency to seek information when it does not affect action; more information is not always better. For example, asking an independent expert to prepare a report on an issue when two other reports already exist in respect of the same issue.
- Ostrich effect. The decision to ignore dangerous or negative information by ‘burying one’s head in the sand’.
- Outcome bias. Judging a decision based on the eventual outcome rather than on the quality of the decision at the time it was made. This is similar to hindsight bias whereby the ‘correct’ choice at the time the decision was originally made appears obvious subsequently.
- Recency. The tendency to weigh the latest information more heavily than older data. Therefore, if you have a run of recent cases with the same issue appearing within it, you are likely to conclude that this is an issue which is affecting other decision-makers within your tribunal.
- Salience or saliency bias. Our tendency to focus on the most easily recognizable features of a person or behaviour. For example, when trying to explain someone’s behaviour we usually only have observable external information to eliminate risks about that individual. This leads to these salient factors being more influential in determining the cause of the person’s behaviour.
- Selective perception. Tied to cognitive dissonance, this is where we allow our expectations to influence how we perceive the world. For example, if we have the opinion that the advocate due before us is incompetent, from prior dealings with them, we are likely to focus our attention on their faults and miss any positive behaviours.
- Survivorship bias. An error that comes from focusing only on surviving examples or past successes rather than on past failures, causing us to misjudge situations. For example, rather than focus on those cases you concluded which were upheld on appeal, study instead those cases where you were overturned.
- Zero-risk bias. People love certainty even if it’s counterproductive, hence we ignore probability and focus on the potential impact were the event to occur. We wish to entirely eliminate risks even when an alternative option might produce a greater reduction in risk overall. For example, choosing to keep the status quo rather than grant an application where there is a risk that things might go badly wrong which will not happen if the current situation is maintained. Of course, there is the possibility things will be even better but zero-risk bias makes us wish to eliminate risks completely.