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These examples are not meant to show that ADMs are inherently dangerous but rather how embedded it is in our society. ADMs are tools that are usually built with good intentions and have positive benefits. ADMs are often built to make consistent and accurate decisions at scale. Humans tend to make inconsistent and biased decisions. Other examples can be found in Appendix 7.

ADMs have also allowed the influx of web-scale demands of internet access. Each job opening receives applications. Google receives 3 million resumes a year and hired 7, It is much more efficient to automatically screen candidates. This increases the productivity of each recruiter i. Going back to the example of mortgage loans in section 3.

Section 6 discusses the profit trade off more. The diagram below shows where bias creeps into the machine learning process and where interventions could be made [9]. Most of developers do not intentionally build bias into the models.

Bias can appear because of the data, algorithm, and how the model is used. Unrepresentative training data is by definition biased. The model will perform poorly, regardless of whether it is used to discriminate against a certain group or individual. Unrepresentative dataset could arise for various reasons such as incomplete data collection and oversampling a subpopulation.

Biased datasets are data recording past biased decisions. Models trained on this data will simply reflect and reinforce the bias. The model building process involves selecting what features to include in the model and what algorithm to use. In most cases, the model builder e. What features get selected will incorporate biases of the builder. In most algorithms, there is a cost function that is being minimized. In the case of linear regression, it is the squared-deviations.

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The cost function is usually set to maximize accuracy but it can be configured to optimize different metrics. However, usually it is set to maximize accuracy which often reinforces bias patterns embedded in the dataset. How the model is used and evaluated also lead to bias. In section 3. Different thresholds reflect different fairness measurements. The performance of models are also evaluated by using different metrics, often accuracy is used.

Fairness Center

The use of fairness metrics, or the lack of, also leads to bias. Bias mitigating methods are categorized by the stage of intervention during the machine learning pipeline. Fair pre-processing methods tackle biases present in data.

Fairness must come first

The diagram below is a good overview of the more common methods [10]. We will not dive into each specific method since it will be too technical. One pre-processing method that is not listed below is collecting more useful data. The issue for businesses right now are awareness, uncertainty about standards, lack of qualified methods to ability to solve biases [11]. This is where most efforts are being poured into. There are several working groups and institutes that are leading the charge to define fairness and educate both private and public constituents e.

However, one issue that will become more prominent as the former are addressed is how it will impact profits. Profits motivate firms, amidst regulatory, moral, and societal constraints. As section 4. Firms are not naturally incentivized to ensure fair machine learning algorithms. One way to think about how predictions link to profits is illustrated below with confusion matrices.

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A machine learning algorithms value is being able to increase the number of true positives and true negatives, which each have a value attached. Each false positive and false negative is costly. The value assigned to each depends on each context. A false negative is more costly in medical situations while a false positive is costlier in death penalty decisions.

Expected value is profits that businesses can expect from using the algorithm. The more accurate the model, the higher the profits. Bias mitigating methods that decrease accuracy will face resistance from businesses. Unfortunately, there is usually a tradeoff between accuracy and fairness. Different bias mitigating methods have different negative impact on accuracy. Pre-processing methods have the least impact on accuracy and hence are more valuable for businesses [12].

Collecting more data may increase accuracy further while also improving fairness. However, data collection and processing is expensive and the most time consuming part of the machine learning pipeline. For example, there is no guideline or best practice on what additional data to collect, partly because each use case is requires different data. Being aware and clear about this trade off is important, because business leaders can make a more informed, and hopefully better, choice. Regulated domains.

Disparate treatment can be thought of as procedural fairness. The underlying philosophy is equality of opportunity.

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Disparate impact is distributive justice. There is tension between these two goals. Relative to human screeners, hiring algorithms yields candidates that are more diverse and likely to pass interviews, accept job offers, and perform better at work [13]. Using ADMs in consumer lending decisions will increase long-run profits while also reducing bias against older and immigrant borrowers [14].

Replacing judges with algorithms to predict recidivism increases social welfare. Each job opening receives applications [16]. Google receives 3 million resumes a year and hired 7, [17]. The goal of the workshop was to produce a report or white paper that articulates best practices and research challenges with regards to fairness and economics, as well as provides a sense of direction for the field.

A workshop report is in progress. Michael D. Discussant: Aaron Roth, University of Pennsylvania slides. John E. Discussant: Rakesh Vohra, University of Pennsylvania slides. The CCC will cover travel expenses for all participants who desire it. In general, standard Federal travel policies apply: CCC will reimburse for non-refundable economy airfare on U. Flag carriers; and no alcohol will be covered.

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Additional questions about the reimbursement policy should be directed to Ann Drobnis, CCC Director adrobnis [at] cra. This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful. You can adjust all of your cookie settings. Economics and Fairness. Workshop Report. Should we be concerned with the concentration of data in a small set of hands?

If so, how do we guard against this? Algorithmic Recommendation In many settings algorithms make recommendations rather than decisions. How do humans interpret and make use of these recommendations?