Private and public institutions already rely on ML to perform basic and complex functions with greater efficiency and accuracy than people. Growing datasets and ever-improving hardware, in combination with ongoing advances in computer science and statistics, ensure that these methods will only become more ubiquitous in the years to come. We argue that the potential benefits of successful iML strategies are more varied and numerous than Kleinberg et al. acknowledge. In the case of algorithmic auditing, iML can help ensure the fair, accountable, and transparent application of complex statistical models in high-stakes applications like criminal justice and healthcare.

If I could lose my points at any time and just as easily take the leader's points at any time - why play at all? Similarly, sometimes when you game guide click to get a power up you instead randomly lose half your points. It is new so hopefully these kinks will be worked out but, even if they are, I honestly don't see a reason to play this over Gimkit. To add another element to the game, I included 3 ‘lifelines’ or hints to help get the correct answer. These appear on the right of the screen and, while initial faded, become available as you accumulate score.

games

Recent advances in ML have raised a number of pressing questions regarding the epistemic status of algorithmic outputs. One of the most hotly debated topics in this emerging discourse is the role of explainability. The call for more explainable algorithms has been especially urgent in areas like clinical medicine (Watson et al. 2019) and military operations (Gunning 2017), where user trust is essential and errors could be catastrophic. This has led to a number of international policy frameworks that recommend explainability as a requirement for any ML system (Floridi and Cowls 2019).

First, the talk of algorithmic “decisions” is an anthropomorphic trope granting statistical models a degree of autonomy that dangerously downplays the true role of human agency in sociotechnical systems (Watson 2019). Second, we may want to explain not just the top label selected by a classifier—the so-called “decision”—but also the complete probability distribution over possible labels. In a regression context, we may want to explain a prediction interval in addition to a mere point estimate. Finally, there are good pragmatic reasons to take a causal approach to this problem.

Game theory is largely attributed to the work in the 1940s of mathematician John von Neumann and economist Oskar Morgenstern. It was developed extensively by many other researchers and scholars in the 1950s. It remains an area of active research and applied science to this day. The games may involve how two competitor firms will react to price cuts by the other, whether a firm should acquire another, or how traders in a stock market may react to price changes. In theoretic terms, these games may be categorized as prisoner's dilemmas, the dictator game, the hawk-and-dove, and Bach or Stravinsky. Game theory may analyze a set of behaviors but it cannot truly forecast the human element.

We can express the Shapley value of a player as the expected value of the weighted marginal contribution to a random coalition S sampled uniformly from all possible coalitions excluding that player, rather than an exhaustive weighted sum. A sampling estimator of this expectation is by nature unbiased, so this can be used as an alternative to the permutation estimator in approximating attributions with confidence intervals. Zerilli et al. (2019) argue that proponents of iML place an unreasonable burden on algorithms by demanding that they not only perform better and faster than humans, but explain why they do so as well.

What Is a Nash Equilibrium?

In the 1970s, game theory was extensively applied in biology, largely as a result of the work of John Maynard Smith and his evolutionarily stable strategy. In addition, the concepts of correlated equilibrium, trembling hand perfection and common knowledge[a] were introduced and analyzed. Game theory has been applied to a wide variety of situations in which the choices of players interact to affect the outcome.

Clicking this will remove half of the options leaving only the correct answer and 3 alternatives. The second lifeline, changes the images shown and reorders the display. One is the correct response and the other 7 are randomly selected from the other images in the content set. The second lifeline, re-shuffles and reselects the 7 alternatives. As this one is quite useful, it is can only be used a few times.

Computer Science > Machine Learning

5.2.2, although guarantees cannot generally be provided in such instances without additional assumptions. 5, we articulate the rules of the explanation game and outline the procedure in pseudocode. The payoffs of the game are generally taken to represent the utility of individual players. A symmetric game is a game where each player earns the same payoff when making the same choice.

For instance, neoclassical economics struggled to explain entrepreneurial anticipation and could not handle the imperfect competition. Game theory turned attention away from steady-state equilibrium toward the market process. Generally, there can be more than one equilibrium in a game. However, this usually occurs in games with more complex elements than two choices by two players.

However, the dickering that they do to reach this point will be noncooperative. Similarly, when people bid independently at an auction they are playing a noncooperative game, even though the high bidder agrees to complete the purchase. It would be surprising if any one theory could address such an enormous range of “games,” and in fact there is no single game theory. A number of theories have been proposed, each applicable to different situations and each with its own concepts of what constitutes a solution.

These algorithms include the use of automated reasoners (for example, SAT, SMT or MILP solvers), or dedicated algorithms for families of classifiers for which computing one explanation is tractable. To the best of our knowledge, there exists no proof that the solution to a subsampled objective function of the form in Eq. 14 is an estimator (unbiased or otherwise) of the Shapley values.