siliconindia | | July 20199veillance, camera surveillance (with facial recognition) raises human rights concerns. While human intervention is possible, due to the sheer volume involved, intervention is likely to happen only in the rarest of cases. Social media manipulation by state actors to influence people's opinions gives politicians the ability to provoke people's emotions to win elections. Facebook, YouTube and Twitter decide what shows up on a user's home feed despite who their friends are. The algorithms match the user with their preferences irrespective of whether it is good or bad. As an example, if an individual is extreme-ly radicalized, they are likely to get friend recommenda-tions or feeds that are in agreement with their radicalized views. This makes the problem worse and doesn't help to fix the core issue. This positive reinforcement makes the society very polarized as `right wing' and `left wing' to the point that people begin to vehemently dislike those with opposing viewpoints.Several initiatives are currently underway to improve the situation. In some cases, a knee-jerk reaction from policy makers triggers events that lead to change. Califor-nia, for example, has banned the use of facial recognition software by police and other agencies. There are other actors looking for an active solution to the problem of unaccountable algorithms and data manipulation. Research Institutes like Data & Society focus on the social and cultural issues arising from data-centric tech-nological development to raise awareness and debate around algorithmic accountability, media manipulation & disinformation online, and so on. While deep learning algorithms are relatively inscru-table, traditional machine learning algorithms continue to provide the right solution for many problems. There is a focused effort on ensuring algorithmic accountability on traditional machine learning systems in order to certify a machine learning model as fit for a purpose. Companies are now focusing on data lineage to ensure the integrity of data that is being used to build machine learning models. Data lineage plays a central role in data warehouses for establishing data integrity and trust. Net-flix has built a centralized lineage service to better under-stand the movement and evolution of data and related data artefacts within the company's data warehouse, from the initial ingestion of trillions of events through multistage ETLs, reports, and dashboards.Companies are actively developing monitoring solu-tions for machine learning systems in production to en-sure that the models in production don't decay over time due to data poisoning or become biased due to bad data. Fairness, accountability, and privacy are a critical part of our society and social ecosystem. Solutions like ma-chine learning keep increasing in importance & reach and are being used for use cases both good and bad. Rules and expectations around fairness and accountability will increase as more of our daily lives are impacted by al-gorithms that work purely from data. People, companies and governments need to engage in ethical and fairness debates to evolve legal frameworks and social norms that build fairness and accountability into the systems that impact people's lives like never before. If the industry, government and members of the public don't take the ini-tiative, the future will very well be ruled by algorithmic overlords with no easy recourse. While deep learning algorithms are relatively inscrutable, traditional machine learning algorithms continue to provide the right solution for many problemsMukund Rajamannar
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