View: Why India should follow Swachh Data Mission to collect the right data for policymakingLatest Updates - CA Mitesh By Rekha JainThe defining feature of NITI Aayog’s policy for artificial intelligence (AI) is its focus on social sectors and empowering citizens. This is highlighted by its ‘AI for All’ motto. Some of its pilot deployments were cited in the RAISE (Responsible AI for Social Empowerment) 2020 virtual global summit on October 5-9.While algorithms are integral to AI applications, an equally important part is data. AI applications require large, complex datasets comprising both structured and unstructured data across several domains. Large datasets are broken into training data and testing data. Using training data, the software ‘learns’ to detect patterns in the underlying data. Subsequently, the testing data is used to validate the learnt algorithms or pattern-matching techniques. These are then fine-tuned to develop more robust AI solutions for prediction, planning and allocation of resources.Datasets that may be used for public policy purposes are available with government departments and ministries. While large amounts of digital data are available with GoI, such data should be made available publicly, with due focus on the privacy of individuals as per the Personal Data Protection Bill.The existing National Data-Sharing and Accessibility Policy (NDSAP) for facilitating access to GoI data and data created with public funds can be the basis for access to government datasets.As per NDSAP, a variety of data (raw, derived, spatial, non-spatial, etc) is mandated to be in a machine-readable form, periodically updated and proactively available. Seeking proposals for AI applications in the relevant domains can then be done through hackathons, regular processes or contests, using the available datasets.To be able to use this data for AI, not only does the data need to be accurate and properly formatted, it also needs to be representative of the underlying context. For example, it has been found that AI applications in the US based on testing datasets that predominantly contained facial data of White people did not recognise Black people. Similarly, if training datasets have data only from a particular region in India, then predictions for another region may not be accurate. Such requirements are more critical when dealing with different socioeconomic categories. Government collection of data often does not consider this dimension explicitly.Further, NDSAP data is available at different levels of aggregation. Some of the data is at the district level, others at the state level, with the latter not being available at a lower level of aggregation. Most of the data is dated. Some datasets do not indicate the year of the data, essentially rendering it useless for any decision-making.Some datasets do not have raw data, but only percentages (as a number, not as a formula in Excel), limiting flexibility in analysis. Also, due to the fragmented nature of the data and differing formats across departments, it is not possible to link such data for analytics purposes without a lot of heavylifting.So, analysis has to be conducted across ministries — which is often not possible. Since there is little focus on data quality at the stage of capture, its quality is suspect, as often data is first captured manually and then transcribed into a digital format.Using AI for policymaking, therefore, requires an overhaul of the processes for data collection, storage, standards, formatting and dissemination. Since data is the basic building block for AI, the focus of ‘AI for All’ should also be to identify mechanisms for developing schema for the metadata, identifying the interlinkages between data elements, and for collecting, verifying and updating the data in all ministries and government departments. Such a mission could be named, Swachh Data Mission, with the same — if not more —rigour and focus of the Swachh Bharat mission.(The writer is principal adviser, Broadband India Forum) Chartered Accountant For consultng. Contact Us: http://bit.ly/bombay-ca from CA Mitesh and Associates Chartered Accountants Mumbai https://bit.ly/30UgbW3 via CA in Kandivali West
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