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Introduction

Assisted Reproductive Technology (“ART”) refers to a diverse set of medical procedures designed to aid individuals or couples in achieving pregnancy when conventional methods are unsuccessful. This umbrella term encompasses various fertility treatments, including in vitro fertilization (IVF), intrauterine insemination (IUI), and gamete and embryo manipulation. ART procedures involve the manipulation of both male and female reproductive components to facilitate conception.

The dynamic landscape of data flows within the healthcare sector, notably in the realm of ART, demands a nuanced understanding of the complex interplay between privacy regulations and medical practices. In this context, the Information Technology (Reasonable Security Practices And Procedures And Sensitive Personal Data Or Information) Rules, 2011, play a pivotal role, designating health information as "sensitive personal data or information" and underscoring the importance of safeguarding individuals' privacy. This sensitivity is particularly pronounced in the ART sector, where an array of personal data, ranging from medical records to genetic information, is collected and processed. The recent Assisted Reproductive Technology (Regulation) Act, 2021, in conjunction with the Digital Personal Data Protection Act, 2023, establishes a framework for the regulation of ART clinics and banks, presenting a layered approach to data protection.

A note on data generated by ART

Data flows in any sector are scarcely uniform and often not easily classified under straight-jacket categories. Consequently, mapping and identifying data and its types become pivotal. It is believed that most data flows in the healthcare sector are highly sensitive and personal in nature, which may severely compromise the privacy and safety of an individual if breached. The Information Technology (Reasonable Security Practices And Procedures And Sensitive Personal Data Or Information) Rules, 2011 (“SPDI Rules”) categorizes any information pertaining to physical, physiological, mental conditions or medical records and history as “sensitive personal data or information”; this definition is broad enough to encompass any data collected by any ART facility or equipment. These include any information collected during the screening of patients, pertaining to ovulation and menstrual cycles, follicle and sperm count, ultrasound results, blood work etc. It also includes pre-implantation genetic testing on embryos to detect any genetic abnormality. 

But data flows extend beyond mere medical procedures and technology. Health data also involves any medical procedures undertaken, the amount of medicine and drugs administered during any procedure, its resultant side effects, recovery etc. Any processing of the above-mentioned information, in turn, may generate more personal data points relating to an individual’s political affiliations, race, ethnicity, genetic data such as biometrics and DNA etc.; It is seen that different ethnicities and races react differently to the same/similar medication and have different propensities to genetic diseases. Further, it is to be noted that data is not only collected by professionals but also by intelligent equipment like AI which may be employed by any facility to render their service. Additionally, dissemination of information under exceptional circumstances (e.g. medical emergency) also affects how data may be classified. Considerations are further nuanced when the fundamental right to identity of a child conceived and born via ART may be in conflict with the fundamental right to privacy of a donor to remain anonymous. 

Intersection of Privacy laws and ART laws: 

In India, ART technology is regulated by the Assisted Reproductive Technology (Regulation) Act, 2021 (“ART Act”). With this, the Union aims to regulate and supervise assisted reproductive technology clinics and ART banks, prevent misuse and ensure safe and ethical practice of assisted reproductive technology services. When read with the Digital Personal Data Protection Act, 2023 (“DPDP Act”) and other ancillary guidelines, the two legislations provide some framework regulations for the digital privacy of health-based apps.

The ART Act establishes a National Assisted Reproductive Technology and Surrogacy Registry (“National Registry”) which acts as a central database for all clinics and banks and their nature of services. The Act also establishes a National Assisted Reproductive Technology and Surrogacy Board (“National Board”)  under the Surrogacy Act to monitor the implementation of the act and advise the central government on policy matters. It also supervises the functioning of the National Registry, liaises with State Boards and curates a code of conduct for professionals working in ART clinics and banks. Under the DPDP Act, these bodies (i.e. National Board, State Board, ART clinics and banks) are most likely classified as data fiduciaries (primarily clinics and banks), data processors (these may include National Board and State boards) or an amalgamation of both (these include any appropriate authority established under the ART Act for investigation of complaints, suspend or cancellation of registration of clinics etc.) depending on the nature of work undertaken by them. If so classified, then the duties and liabilities of data fiduciaries and processors would necessarily apply to these bodies. As a result, all bodies would necessarily have to adopt Privacy Enhancing Technologies (PETs) and other organizational measures to ensure compliance with privacy laws in place. This may be considered one of the most critical considerations of any ART facility since any data collected by them would be sensitive personal data pertaining to health, regulated by the Information Technology (Reasonable Security Practices And Procedures And Sensitive Personal Data Or Information) Rules, 2011 (“SPDI Rules 2011”). These rules provide for how sensitive personal data or information are to be collected, handled and processed by anyone.

The ART Act independently also provides for the duties of ART clinics and banks in the country. ART clinics and banks are required to inform the commissioning couple/woman of all procedures undertaken and all costs, risks, advantages, and side effects of their selected procedure. It mandatorily ensures that all information collected by such clinics and banks to not informed to anyone except the database established by the National Registry or in cases of medical emergency or on order of court. Data collected by clinics and banks (these include details on donor oocytes, sperm or embryos used or unused) are required to be detailed and must be submitted to the National Registry online. ART banks are also required to collect personal information of donors including name, Aadhar number, address and any other details. By mandating online submission, the ART Act is harmonized with the DPDP Act, which regulates all digital personal data and emphasises free, informed consent.

Conclusion

With the increase in active opt-ins for ART, data privacy becomes a vital consideration for all healthcare facilities and professionals. Safeguard measures are not only required on a corporate level but also on a governmental level. It is to be noted that in the 262 Session of the Rajya Sabha, the Ministry of Electronics and Information Technology reported 165 data breach incidents involving citizen data from January 2018 to October 2023 from the Central Identities Data Repository despite publicly denying. This discovery puts into question the safety and integrity of data that may be submitted to the National Registry database, especially given the type of data (both personal and sensitive information) it aims to collate. At present the ART Act is well supported by the DPDP Act. However, further judicial and legislative deliberations are required to effectively regulate and balance the interests of all stakeholders.

References

• The Information Technology (Reasonable Security Practices And Procedures And Sensitive Personal Data Or Information) Rules, 2011
• Caring for Intimate Data in Fertility Technologies https://dl.acm.org/doi/pdf/10.1145/3411764.3445132 
• Digital Personal Data Protection Act, 2023
• https://www.wolterskluwer.com/en/expert-insights/pharmacogenomics-and-race-can-heritage-affect-drug-disposition  

Introduction

Criminal justice in India is majorly governed by three laws which are – Indian Penal Code, Criminal Procedure Code and Indian Evidence Act. The centre, on 11th August 2023’ Friday, proposes a new bill in parliament Friday, which is replacing the country’s major criminal laws, i.e. Indian Penal Code, Criminal Procedure Code and Indian Evidence Act.

The following three bills are being proposed to replace major criminal laws in the country:

• The Bharatiya Nyaya Sanhita Bill, 2023 to replace Indian Penal Code 1860.
• The Bharatiya Nagrik Suraksha Sanhita Bill, 2023, to replace The Code Of Criminal Procedure, 1973.
• The Bharatiya Sakshya Bill, 2023, to replace The Indian Evidence Act 1872.

Cyber law-oriented view of the new shift in criminal lawNotable changes:Bharatiya Nyaya Sanhita Bill, 2023  Indian Penal Code 1860.

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‍Way ahead for digitalisation

‍The new laws aim to enhance the utilisation of digital services in court systems, it facilitates online registration of FIR, Online filing of the charge sheet,  serving summons in electronic mode, trial and proceedings in electronic mode etc.  The new bills also allow the virtual appearance of witnesses, accused, experts, and victims in some instances. This shift will lead to the adoption of technology in courts and all courts to be computerised in the upcoming time.

‍Enhanced recognition of electronic records

‍With the change in lifestyle in terms of the digital sphere, significance is given to recognising electronic records as equal to paper records.

‍Conclusion

‍The criminal laws of the country play a significant role in establishing law & order and providing justice. The criminal laws of India were the old laws existing under British rule. There have been several amendments to criminal laws to deal with the growing crimes and new aspects. However, there was a need for well-established criminal laws which are in accordance with the present era. The step of the legislature by centralising all criminal laws in their new form and introducing three bills is a good approach which will ultimately strengthen the criminal justice system in India, and it will also facilitate the use of technology in the court system.

Introduction

Search engines have become indispensable in our daily lives, allowing us to find information instantly by entering keywords or phrases. Using the prompt "search Google or type a URL" reflects just how seamless this journey to knowledge has become. With millions of searches conducted every second, and Google handling over 6.3 million searches per minute as of 2023 (Statista), one critical question arises: do search engines prioritise results based on user preferences and past behaviours, or are they truly unbiased?

Understanding AI Bias in Search Algorithms

AI bias is also known as machine learning bias or algorithm bias. It refers to the occurrence of biased results due to human biases that deviate from the original training data or AI algorithm which leads to distortion of outputs and creation of potentially harmful outcomes. The sources of this bias are algorithmic bias, data bias and interpretation bias which emerge from user history, geographical data, and even broader societal biases in training data. 

Common biases include excluding certain groups of people from opportunities because of AI bias. In healthcare, underrepresenting data of women or minority groups can skew predictive AI algorithms. While AI helps streamline the automation of resume scanning during a search to help identify ideal candidates, the information requested and answers screened out can result in biased outcomes due to a biased dataset or any other bias in the input data.

Case in Point: Google’s "Helpful" Results and Its Impact 

Google optimises results by analysing user interactions to determine satisfaction with specific types of content. This data-driven approach forms ‘filter bubbles’ by repeatedly displaying content that aligns with a user’s preferences, regardless of factual accuracy. While this can create a more personalised experience, it risks confining users to a limited view, excluding diverse perspectives or alternative viewpoints.

The personal and societal impacts of such biases are significant. At an individual level, filter bubbles can influence decision-making, perceptions, and even mental health. On a societal level, these biases can reinforce stereotypes, polarise opinions, and shape collective narratives. There is also a growing concern that these biases may promote misinformation or limit users’ exposure to diverse perspectives, all stemming from the inherent bias in search algorithms.

Policy Challenges and Regulatory Measures

Regulating emerging technologies like AI, especially in search engine algorithms, presents significant challenges due to their intricate, proprietary nature. Traditional regulatory frameworks struggle to keep up with them as existing laws were not designed to address the nuances of algorithm-driven platforms. Regulatory bodies are pushing for transparency and accountability in AI-powered search algorithms to counter biases and ensure fairness globally. For example, the EU’s Artificial Intelligence Act aims to establish a regulatory framework that will categorise AI systems based on risk and enforces strict standards for transparency, accountability, and fairness, especially for high-risk AI applications, which may include search engines. India has proposed the Digital India Act in 2023 which will define and regulate High-risk AI. 

Efforts include ethical guidelines emphasising fairness, accountability, and transparency in information prioritisation. However, a complex regulatory landscape could hinder market entrants, highlighting the need for adaptable, balanced frameworks that protect user interests without stifling innovation.

CyberPeace Insights

In a world where search engines are gateways to knowledge, ensuring unbiased, accurate, and diverse information access is crucial. True objectivity remains elusive as AI-driven algorithms tend to personalise results based on user preferences and past behaviour, often creating a biased view of the web. Filter bubbles, which reinforce individual perspectives, can obscure factual accuracy and limit exposure to diverse viewpoints. Addressing this bias requires efforts from both users and companies. Users should diversify sources and verify information, while companies should enhance transparency and regularly audit algorithms for biases. Together, these actions can promote a more equitable, accurate, and unbiased search experience for all users. 

References 

• https://www.bbc.com/future/article/20241101-how-online-photos-and-videos-alter-the-way-you-think 
• https://www.bbc.com/future/article/20241031-how-google-tells-you-what-you-want-to-hear
• https://www.ibm.com/topics/ai-bias#:~:text=In%20healthcare%2C%20underrepresenting%20data%20of,can%20skew%20predictive%20AI%20algorithms

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