Adjusting Algorithms

Algorithm, Malgorithm, Jalgorithm?   Fairness in Judgement. 

Right, algorithm just means a standardized method.    Like iany tool can be used or misused. Good piece here.

Misnomer and Malgorithms  By Robin K. Hill  in CACM

In response to a previous piece on the articulation of design responsibility [Hill2018], by which I mean the egregious practice of casually attributing judgment and volition to programs, I've received some comments. My view is that the attribution, in our locutions, of decision-making power to certain applications of programs and algorithms is wrong in both senses of "wrong"—both false and harmful.

The most obvious, and most misleading, instance of malarticulation is the trending use of "algorithm". One or two comments mentioned the common modern use of that word to mean an agent that makes (bad) judgments, giving rise to claims that that technology is not value-neutral. The concern is valid but the connotation hangs on context, and the implications of the literal assertion are dangerous. "Oh, well, sure," educated people will say, "We agree that tech is technically neutral." Yes, it's technically neutral. In fact, technically, it's nothing more than technical, and therefore nothing more than neutral.

This needs to be cleared up. Computer science knows the algorithm as an objective computational object, breathtaking and beautiful, an abstract imperative structure (so I claim [Hill2016]), deterministic and independent of context. I will call this objective procedure, a mechanism that performs calculations under a decision structure, the i-algorithm; maybe we can think of the i as "imperative structure". But the public knows the algorithm as a mysterious agent making dubious decisions, a source of judgments, supposed to be reasonable, on complex issues in real life. I will call this subjective procedure the j-algorithm; we can think of the j as "judge". These are homonyms but not synonyms, and we understand that. Computer scientists, told that an i-algorithm is political, simply code-switch to the homonym j-algorithm, the thing that assesses parole requests and loan applications (poorly), in order to continue the communication. This communication infelicity is not new—scientists have to put up with "bug", "exponential", "schizo", and other abuses of terminology. The problem with "algorithm" is that the two senses of the word are, in a way, contradictory, and in exactly the way that matters.  .. " 

Worrying What others Think of Us

Useful piece, we all practice this. 

Why We Should Stop Worrying About What Others Think of Us
Mar 31, 2020 Research North America  In Knowledge@Wharton

Standing in the spotlight can be daunting. Giving that third-quarter report to shareholders, pitching your idea at a team meeting, even competing in the state fair to win first place with a batch of your best chocolate chip cookies makes most people feel the uncomfortable pressure of being judged. But there’s new scientific evidence to bolster the anecdotal advice that mom always gave you: Just relax and do your best.

A study co-authored by Alice Moon, Wharton professor of operations, information and decisions, finds that when people perform tasks in front of others, they tend to believe they are being judged harshly on their performance. But in reality, actors are much harder on themselves than the observers who are watching them. People also worry that they will be unfairly judged on the whole based on a single part. For example, a driver who can’t parallel park worries that people watching him from the sidewalk now think he’s lousy at all aspects of driving. But in reality, observers would evaluate his skills behind the wheel based on a number of measures, such as his awareness of blind spots, maintaining a safe distance, his attention to road signs, his use of turn signals, etc.

All that worry and stress can lead to what Moon calls the “overblown implications effect.” When people are so preoccupied with the judgment of others, they tend to believe that that judgment is far worse than it is. Through a series of experiments, Moon and her colleagues found that actors consistently overblow their failures — and even their successes — because they often don’t see things from the broader view of the observer. “Actors see their own performance as having more evaluative impact on observers than it actually does.… Successful parallel parkers will be mistaken in thinking their full driving skills are on display,” the researchers write in their paper titled, “The Overblown Implications Effect.”

Moon wrote the paper with Clayton Critcher, associate professor of marketing at the Haas School of Business at the University of California, Berkeley, and Muping Gan, a former UC Berkeley graduate researcher who now works for YouTube. Moon recently discussed the implications of their research with Knowledge@Wharton.

Knowledge@Wharton: What piqued your interest in this topic?  ... '

Collaboration of Human and Machine

A considerable 'practice'  article in Communications of the ACM.   Where are we and where are we going?   Not too far off from further mixing computing and human judgement.  ....  But the human side is judging the advice and interaction differently.   But how?

The Effects of Mixing Machine Learning and Human Judgment

September 16, 2019
Volume 17, issue 4

Collaboration between humans and machines does not necessarily lead to better outcomes.
By 

In 1997 IBM's Deep Blue software beat the World Chess Champion Garry Kasparov in a series of six matches. Since then, other programs have beaten human players in games ranging from Jeopardy to Go. Inspired by his loss, Kasparov decided in 2005 to test the success of Human+AI pairs in an online chess tournament.2 He found that the Human+AI team bested the solo human. More surprisingly, he also found that the Human+AI team bested the solo computer, even though the machine outperformed humans.

Researchers explain this phenomenon by emphasizing that humans and machines excel in different dimensions of intelligence.9 Human chess players do well with long-term chess strategies, but they perform poorly at assessing the millions of possible configurations of pieces. The opposite holds for machines. Because of these differences, combining human and machine intelligence produces better outcomes than when each works separately. People also view this form of collaboration between humans and machines as a possible way to mitigate the problems of bias in machine learning, a problem that has taken center stage in recent months.12

We decided to investigate this type of collaboration between humans and machines using risk-assessment algorithms as a case study. In particular, we looked at the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm, a well-known (perhaps infamous) risk-prediction system, and its effect on human decisions about risk. Many state courts use algorithms such as COMPAS to predict defendants' risk of recidivism, and these results inform bail, sentencing, and parole decisions.

Prior work on risk-assessment algorithms has focused on their accuracy and fairness, but it has not addressed their interactions with human decision makers who serve as the final arbitrators. In one study from 2018, Julia Dressel and Hany Farid compared risk assessments from the COMPAS software and Amazon Mechanical Turk workers, and found that the algorithm and the humans achieved similar levels of accuracy and fairness.6 This study signals an important shift in the literature on risk-assessment instruments by incorporating human subjects to contextualize the accuracy and fairness of the algorithms. Dressel and Farid's study, however, divorces the human decision makers and the algorithm when, in fact, the current model indicates that humans and algorithms would work in tandem.

Our work, consisting of two experiments, therefore first explores the influence of algorithmic risk assessments on human decision-making and finds that providing the algorithm's predictions does not significantly affect human assessments of recidivism. The follow-up experiment, however, demonstrates that algorithmic risk scores act as anchors that induce a cognitive bias: If we change the risk prediction made by the algorithm, participants assimilate their predictions to the algorithm's score..... " 

Intelligent Enterprise: Judgement, Reasoning and Decisions.

Stephen DeAngelis of Enterra Systems, who we have worked with writes on   The Rise of the Intelligent Enterprise  in LinkedIn: 

" ... We live in the Digital Age. The World Economic Forum has declared data is a resource as valuable as oil. We have watched the rise of digital enterprises (i.e., enterprises whose very existence was impossible until the Internet matured). Today most analysts agree organizations created in the Industrial Age need to undergo digital transformation and become digital enterprises. Some analysts even argue that going digital won’t be enough. To survive, they insist, an organization must become an intelligent enterprise. “In coming years,” explain Paul J.H. Schoemaker, Founder and Executive Chairman of Decision Strategies International, and Philip E. Tetlock (@PTetlock), the Annenberg University Professor at the University of Pennsylvania, “the most intelligent organizations will need to blend technology-enabled insights with a sophisticated understanding of human judgment, reasoning, and choice. Those that do this successfully will have an advantage over their rivals.”[1] .... " 

I add:

Good points about the understanding of human judgement, reasoning and choice.   Ultimately a result has to be inserted into decisions, usually a group of decisions, by a groups of people (or devices) over time, in varying contexts.   How do we continue to refine how decisions are made?