The seed and agricultural biotechnology industries in India: An analysis of industry structure, competition, and policy options

Since the late 1980s, technological advances and policy reforms have opened up new opportunities for growth in India's seed and agricultural biotechnology industries. The impacts of such changes have been significant in India's cotton sector, but less so for the country's main cereal crops, where both yield and output growth rates have been relatively stagnant. Some public policymakers and corporate decisionmakers are confident that the private sector will help reverse these trends, arguing that the right combination of new technological solutions and progressive policy reforms will unleash a significant increase in private investment in productivity-enhancing products and services. The structure of India's seed and agbiotech industries, as well as the policies designed to support their growth, will be a significant determinant of this expected impact. This paper examines the structure of India's cereal seed and agbiotech industries, its potential effects on innovation and social welfare, and the policies that may improve both industry performance and the delivery of new technologies to resource-poor, small-scale farmers in India's cereal production systems. We focus our analysis on indicators and scenarios within India's agricultural innovation market for improved seed and agricultural biotechnology products. This market includes firms engaged in the development, commercialization, and marketing of new seed-based technologies; it is characterized by a high level of knowledge intensity, relatively high levels of R&D investment, significant barriers to entry, significant levels of regulation, and relatively few products in the market. And it is within this market that factors such as strategic corporate behavior and public policy can affect the balance between a socially desirable rate of innovation, on the one hand, and a socially desirable distribution of the gains from innovation among consumers, farmers, and innovators, on the other hand.Seed markets, Agricultural biotechnology, industrial organization, Cereal crops,

Placing Children with Relatives: The Case for a Clear Rationale for Separate Foster Care Licensing Standards, Background Check Procedures, and Improved Relative Placement Statutes in Alaska

Policymakers generally agree that if a child cannot live safely with her parents, then the child should be placed expeditiously with a relative. Alaska’s current system for evaluating relative caregivers is overly complicated, creating unnecessary barriers for relatives and increasing the risk of mistakenly denying placement with relatives. This Article argues that Alaska should adopt a three-step approach to achieve better outcomes based on the American Bar Association’s model licensing standards, which are narrowly tailored to evaluate whether a child should be placed with a relative. Additionally, this Article argues that Alaska should repeal its state statute that gives the child welfare agency the ability to establish prima facie evidence to deny a relative if a relative would not be eligible for a foster care license, for two reasons. First, a review of the history of the state’s statutes indicates that the legislature did not intend to provide the Department of Health and Human Services with the current definition of prima facie evidence. Second, Alaska’s current statute is not compliant with the 2016 federal regulations regarding the Indian Child Welfare Act. Lastly, this Article argues that Alaska should adopt a statute clearly delineating the court’s authority to order placement of a child facing foster care with a relative to expedite compliance with relative placement in frontline child welfare practice. Adopting these proposals would reduce barriers and the number of mistakes in frontline child welfare practice, which would increase both the timeliness and the number of children placed with relatives

Environmental Justice in India: The National Green Tribunal and Expert Members

This article argues that the involvement of technical experts in decision making promotes better environmental results while simultaneously recognizing the uncertainty in science. India’s record as a progressive jurisdiction in environmental matters through its proactive judiciary is internationally recognized. The neoteric National Green Tribunal of India (NGT) – officially described as a ‘specialised body equipped with necessary expertise to handle environmental disputes involving multi-disciplinary issues’ – is a forum which offers greater plurality for environmental justice. The NGT, in exercising wide powers, is staffed by judicial and technical expert members who decide cases in an open forum. The experts are ‘central’, rather than ‘marginal’, to the NGT’s decision-making process. This article draws on theoretical insights developed by Lorna Schrefler and Peter Haas to analyze the role of scientific experts as decision makers within the NGT. Unprecedented interview access provides data that grants an insight into the internal decision-making processes of the five benches of the NGT. Reported cases, supported by additional comments of bench members, illustrate the wider policy impact of scientific knowledge and its contribution to the NGT’s decision-making process

Protecting Privacy in Indian Schools: Regulating AI-based Technologies' Design, Development and Deployment

Education is one of the priority areas for the Indian government, where Artificial Intelligence (AI) technologies are touted to bring digital transformation. Several Indian states have also started deploying facial recognition-enabled CCTV cameras, emotion recognition technologies, fingerprint scanners, and Radio frequency identification tags in their schools to provide personalised recommendations, ensure student security, and predict the drop-out rate of students but also provide 360-degree information of a student. Further, Integrating Aadhaar (digital identity card that works on biometric data) across AI technologies and learning and management systems (LMS) renders schools a ‘panopticon’. Certain technologies or systems like Aadhaar, CCTV cameras, GPS Systems, RFID tags, and learning management systems are used primarily for continuous data collection, storage, and retention purposes. Though they cannot be termed AI technologies per se, they are fundamental for designing and developing AI systems like facial, fingerprint, and emotion recognition technologies. The large amount of student data collected speedily through the former technologies is used to create an algorithm for the latter-stated AI systems. Once algorithms are processed using machine learning (ML) techniques, they learn correlations between multiple datasets predicting each student’s identity, decisions, grades, learning growth, tendency to drop out, and other behavioural characteristics. Such autonomous and repetitive collection, processing, storage, and retention of student data without effective data protection legislation endangers student privacy. The algorithmic predictions by AI technologies are an avatar of the data fed into the system. An AI technology is as good as the person collecting the data, processing it for a relevant and valuable output, and regularly evaluating the inputs going inside an AI model. An AI model can produce inaccurate predictions if the person overlooks any relevant data. However, the state, school administrations and parents’ belief in AI technologies as a panacea to student security and educational development overlooks the context in which ‘data practices’ are conducted. A right to privacy in an AI age is inextricably connected to data practices where data gets ‘cooked’. Thus, data protection legislation operating without understanding and regulating such data practices will remain ineffective in safeguarding privacy. The thesis undergoes interdisciplinary research that enables a better understanding of the interplay of data practices of AI technologies with social practices of an Indian school, which the present Indian data protection legislation overlooks, endangering students’ privacy from designing and developing to deploying stages of an AI model. The thesis recommends the Indian legislature frame better legislation equipped for the AI/ML age and the Indian judiciary on evaluating the legality and reasonability of designing, developing, and deploying such technologies in schools