OASI, the first search engine to find the algorithms that governments and companies use on citizens

• Created by Eticas Foundation, the Observatory of Algorithms with Social Impact, OASI, collects information from dozens of algorithms used by Public Administrations and companies around the world to learn more about their social impact.
  
• The objective is to give public access to information on both governments and company algorithms, and to know who uses them, who develops them, what threats they represent and if they have been audited, among other characteristics.

• Algorithm bias and discrimination usually occurs based on age, gender, race or disability, among other values, but due to the general lack of transparency, it is still not possible to know all its consequences on the affected groups.

Eticas Foundation, a non-profit organization that promotes the responsible use of algorithms and Artificial Intelligence (AI) systems, has created the Observatory of Algorithms with Social Impact (OASI). This Observatory introduces a search engine to know more about the tools that make important automated decisions on citizens, consumers and users around the world.

Currently, both companies and Public Administrations automate decisions thanks to algorithms. However, its development and commissioning does not follow external quality controls, neither it is as transparent as it should be, which leaves the population unprotected. With this search engine, anyone can find out more about these algorithms: who has developed them, who uses them, their scope of application, whether they have been audited, their objectives or their social impact and the threats they represent.

At the moment, OASI collects 57 algorithms, but expects to reach the 100 in the following months. Among them, 24 are being already applied in the USA by the Government and Big Tech companies. For example, ShotSpotter, an algorithm tool deployed by the Oakland Police Department to fight and reduce gun violence through sound-monitoring microphones, and an algorithm to predict potential child abuse and neglect used by Allegheny County, Pennsylvania. Another example from a corporate is Rekognition, Amazon’s facial recognition system, which was audited by the MIT Media Lab in early 2019, and found to perform substantially worse when identifying an individual’s gender if they were female or darker-skinned.

The most common discrimination is on grounds of age, gender, race or disability, produced unintentionally by developers who lack of socioeconomic skills to understand the impact of this technology. In this sense, these engineers design the algorithms based only on technical skills, and since there is no external controls and it seems to be working as expected, the algorithm keep learning from deficient data.

Given the lack of transparency about the functioning of some of these algorithms, Eticas Foundation, apart from the launch of OASI, is developing a project of external audits. The first is VioGén, the algorithm used by the Spanish Interior Ministry to assign risk to women who seek protection after suffering cases of domestic violence. Eticas will conduct an external audit through reverse engineering and administrative data, interviews, reports or design scripts, to collect results at scale. All this with the objective of detecting opportunities for improvement in the protection of these women.

“Despite the existence of algorithmic control and audit methods to ensure that technology respects current regulations and fundamental rights, the Administration and many companies continue to turn a deaf ear to requests for transparency from citizens and institutions,” declared Gemma Galdon, founder of Eticas Foundation. “In addition to OASI, after several years in which we have developed more than a dozen audits for companies such as Alpha Telefónica, the United Nations, Koa Health or the Inter-American Development Bank, we have also published an Guide to Algorithmic Audit so that anyone can perform them. The objective is always to raise awareness, provide transparency and restore confidence in technology, which in itself does not have to be harmful.”

In this sense, algorithms that are trained with machine learning tecnhiques using a large amount of historical data to "teach" them to choose based on past decisions. Usually these data are not representative of the socioeconomic and cultural reality on which they are applied, but on many occasions they reflect an unfair situation which is not intended to be perpetuated. In this way, the algorithm would be technically making "correct" decisions according to its training, even though the reality is that its recommendations or predictions are biased or discriminate.

About Eticas Foundation

Eticas Foundation works to translate into technical specifications the principles that guide society, such as equal opportunities, transparency and non-discrimination which are in the technologies that make automated decisions about our lives. It seeks a balance between changing social values, the technical possibilities of the latest advances and the legal framework. To this end, it audits algorithms, verifies that legal guarantees are applied to the digital world, especially to Artificial Intelligence, and carries out intense work to raise awareness and disseminate the need for responsible, quality technology.

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