Business Investigation Project Report 2 Sample

Business Investigation Project. Ass 2

Management – Report

Introduction

Business Analytics provides ample opportunities to make better decisions, improve private and public life, as well as the environment. However, such opportunities might cause significant ethical challenges. In effect, the extensive use of personal and sensitive (big) data, and the increasing reliance on algorithms (used in AI, Machine Learning and robotics) to analyse them, coupled with the ongoing reduction of human oversight and involvement over those processes, present issues of fairness, responsibility and respect of human rights, among others. Within this framework, data ethics can be utilised to “evaluate moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values).”

Assessment Task:

You are to write short research report (maximum 2000 words) on “Explain and evaluate the key ethical issues confronting the individual/organisation in analysing data.”

For one of the following sectors:

• Social Media Services
• Online Retail Business or Online Services Business
• Human Resources Management
• Banking and Financial Management
• Healthcare
• Automotive (e.g. cars, planes, ships, rails, drones)
• Transports Logistics (e.g. aviation, shipping, rails, trucking, pipelines, warehousing, postal)
• Manufacturing
• Hospitality (e.g. hotels, restaurants, catering)
• Retail (Bricks and Mortar)
• Utility (e.g. electricity, water, gas)
• Energy (e.g. hydro, coal, solar, wind, biomass, gas, nuclear)
• Risks Management (e.g. insurance, any security)
• Real Estate, Building and Construction Management
• Infrastructure Management.

Solution

1. Introduction

The main ethical problems that human resource management (HRM) faces when analysing data are examined in this research. Concerns around privacy, fairness, transparency, accuracy, and employee empowerment arise when HRM depends more and more on data analytics to guide decision-making processes. According to the MBA Assignment Expert overview,  As HR departments access and analyse enormous volumes of private and sensitive data, depending on algorithms and diminishing human control, ethical questions become crucial. In HR data analysis, the preservation of employee privacy appears as a crucial ethical challenge. HRM needs to set up strong data protection policies that guarantee data security, informed consent, and adherence to privacy laws. Fairness and bias also require attention since, if not carefully planned and maintained, algorithms used in hiring, promoting, and evaluating performance have the potential to perpetuate discrimination. HR professionals need to carefully assess algorithms for biases and make sure they adhere to moral and legal requirements. In order to ensure that employees are informed about data collection, usage, and offering tools for accessing and correcting personal information, transparency and informed consent are necessary. Furthermore, ensuring the dependability and quality of HR data is essential for making fair decisions. HRM should make an investment in data quality assurance procedures, routinely review and update data, and take immediate action to correct errors.

2. Ethical Issues Confronting Human Resource Management

2.1 Privacy and Data Protection

The preservation of employee privacy is one of the most important moral dilemmas that HRM (Human Resource Management) faces in data analysis. HR departments have access to a multitude of individual information, including private data like financial and health records (Tambe, Cappelli and Yakubovich, 2019). Since informed consent, data security, and compliance with privacy laws like the General Data Protection Regulation (GDPR) are all important, HRM must design strong data protection policies that emphasise these factors. Strong security measures should be put in place by HRM to protect employee data from unauthorised access, breaches, or misuse in order to maintain ethical data practices. To protect sensitive information, this entails using encryption methods, secure storage systems, and access controls. Regular data audits and assessments can help find weaknesses and guarantee adherence to privacy rules.

Another essential component of privacy protection is obtaining the informed consent of employees. Employees should be informed in a clear and understandable manner about the aim, scope, and potential consequences of data analysis by HRM. Because of this, people can choose how their personal information is collected and used with knowledge (Vrontis et al, 2022). In addition, HRM should give workers ways to exercise their rights, such as by allowing them to access, correct, or delete their data as needed. The HRM industry must adhere to privacy laws like the GDPR. The collection, storage, and processing of personal data must adhere to strict rules laid out in these regulations, which also guarantee that people's right to privacy is upheld. To guarantee that all staff members participating in data analysis are aware of their responsibilities and follow the law, HRM should make an investment in staff training.

2.2 Fairness and Bias

It is crucial to ensure fairness and minimise bias in the techniques used to analyse HRM data. If not carefully constructed and managed, algorithms used in hiring, promoting, and evaluating performance have the potential to unwittingly support discriminatory practices (Newman, Fast and Harmon, 2020). It is crucial for HR experts to carefully examine algorithms for any biases and make sure they adhere to moral and legal requirements. In order to overcome bias concerns and ensure fairness, regular audits and transparent algorithmic decision-making can be crucial. To find any biases ingrained in the algorithms employed in data analysis, HRM should check them proactively. This entails a thorough analysis of the data inputs, algorithm structure, and decision-making outcomes. HR experts can find and correct biases that might negatively affect particular groups or individuals by analysing the effect of various variables on algorithmic results.

Addressing bias and upholding fairness requires transparency, which is a crucial component. HRM should make an effort to clearly explain the data analysis procedures, including the variables taken into account, the weighting schemes, and the standards for making decisions (KHAN et al, 2023). Employees have the chance to express concerns or point out potential biases when there is open communication about the decision-making process. This openness also fosters confidence in the HR procedures. To maintain fairness, algorithms and the results of decision-making must be regularly audited. To find any biases or inconsistencies, HR professionals should periodically evaluate the effectiveness of algorithms, examine the results they provide, and compare them with actual data. If prejudices are found, urgent corrective action must be made to address the problem and lessen any negative effects.

2.3 Transparency and Informed Consent

Ethical HRM data analysis procedures must emphasise transparency and get informed consent. Employees must be completely informed about how their data is collected, stored, and used (Garcia-Arroyo and Osca, 2021). Employee comprehension of how their personal information is used is guaranteed by open communication about data analytics procedures. Before collecting employee data, HRM should put a priority on getting their informed consent, and there should be systems in place to let people access and correct their information. HRM builds trust and exhibits respect for employee autonomy by placing a high priority on transparency and permission. To explain to employees how their data will be gathered, processed, and used, HRM should build clear and understandable communication channels. This entails disclosing details regarding the reason for data analysis, the kinds of data gathered, the amount of time the data will be retained, and any third parties that the data may be shared with (Agarwal, 2021). Employees should have easy access to clear and straightforward privacy rules and data usage guidelines to ensure transparency.

In order to sustain ethical standards, it is essential to get the employees' informed permission. Prior to gathering and analysing employee data, HRM should create procedures to obtain their express consent. This entails being specific about the reasons the information will be used and making sure that staff members have the chance to inquire, get answers, and voluntarily provide consent. With no force or excessive pressure, consent should be obtained while respecting the autonomy of the employees.

2.4 Accuracy and Data Quality

Maintaining the dependability and quality of HR data is essential to enabling HRM to make decisions that are just and efficient (Hamilton and Sodeman, 2020). HRM must devote resources to establishing data quality assurance procedures, routinely reviewing and updating data, and quickly rectifying any eventual mistakes. Relying on inaccurate or insufficient data might result in unfair results and have negative repercussions on employees. HRM must prioritise data integrity and assume accountability for maintaining record correctness in order to uphold ethical data practices. To maintain the integrity of HR data, HRM should invest in effective data quality assurance techniques. To find and fix any errors, inconsistencies, or duplications, this entails installing validation tests, doing data audits, and using data cleaning techniques. HRM can improve the validity and reliability of the analyses done, resulting in better decision-making, by ensuring data accuracy.

Data quality management requires constant data monitoring and updating. HRM should set up procedures to check data correctness on a regular basis, especially when new information or adjustments are made (Alshurideh et al, 2022). By routinely updating data, HRM can keep it reliable and relevant, lowering the chance that choices will be made based on inaccurate or out-of-date information. Maintaining fairness and moral behaviour requires promptly correcting any discovered mistakes. Procedures for handling data errors should be established by HRM, including ways for staff members to submit errors so that HR specialists may look into them and fix them. 

2.5 Employee Empowerment and Algorithmic Oversight

There are unquestionably substantial advantages to using data analytics into HR practices. But it's crucial to keep the relationship between algorithmic judgement and human control under check (Norlander et al, 2021). When analysing data insights, HR professionals need to use critical judgement and keep control of the decision-making process. Employees should have the authority to challenge algorithmic judgements and request human assistance if needed. HRM can successfully reduce the dangers of unethical or unjust outcomes by encouraging algorithmic oversight and employee empowerment. HRM needs to appreciate how crucial human judgement is during the data analysis process. The knowledge and experience of HR professionals should not be replaced by algorithms and data-driven insights; rather, they should be used as tools to help guide decision-making. HR experts can take into account contextual elements, subtleties, and unique circumstances that algorithms might not fully capture by maintaining control over the decision-making process (Cho, Choi and Choi, 2023). This human review ensures that choices adhere to organisational principles and moral standards.

Empowering employees is essential for upholding moral standards. When they believe the results of an algorithm are unfair or unjust, employees should be encouraged to dispute those conclusions and seek clarification. Employees should be given avenues to voice their issues or ask for human assistance, and HRM should build an environment that encourages open communication. HRM should avoid potential biases or discriminatory outcomes that algorithms might unintentionally cause by valuing employee input and actively addressing their concerns. 

2.6 Ethical Decision-Making Frameworks

HRM can use well-established ethical decision-making frameworks, like the Principles for Ethical AI produced by professional associations and university institutions, to solve the ethical issues brought on by data analysis (Ren, Tang and Jackson, 2021). In the context of AI and data analytics, these frameworks offer a structured approach to assist HRM in making moral decisions and place an emphasis on crucial ideas like accountability, transparency, justice, and human values. Organisations can guarantee the proper and ethical use of data by incorporating these ethical principles into HRM practices. Ethical decision-making frameworks give HR professionals a methodical way to handle challenging ethical conundrums. These frameworks often involve a number of processes, such as recognising the ethical issue, compiling pertinent data, taking into account diverse viewpoints, and assessing probable outcomes. Following a set framework enables HRM to make well-informed decisions that put ethics first, guaranteeing that the implications of data analysis are consistent with organisational values and considerate of employee rights.

Specific criteria for guaranteeing responsible and ethical AI and data analytics are provided by the Principles for Ethical AI, which were created by professional organisations and academic institutions. Fairness, accountability, openness, explainability, and human-centeredness are frequently included in these principles. Organisations can lay the groundwork for moral decision-making in the context of data analysis by adopting these ideas into their HRM practices. Establishing a culture of ethical awareness and accountability through the incorporation of ethical principles within HRM practises. The tools and resources required to use these frameworks effectively should be made available to HR practitioners along with training on them (Podgorodnichenko, Edgar and McAndrew, 2020). To promote conversation on ethical issues related to data analysis, regular debates and forums might be set up. As a result, everyone is encouraged to understand their ethical obligations, and HR professionals are prompted to proactively identify and handle any potential ethical issues.

3. Conclusion

In conclusion, careful thought and proactive steps are needed to address the ethical problems that human resource management (HRM) faces when analysing data. The protection of privacy and data is of the utmost significance, necessitating strict rules and procedures to protect employee data and guarantee compliance with privacy laws. By conducting a critical analysis of algorithms and fostering transparency in the decision-making process, HRM must also address issues with fairness and prejudice. The foundation of trust and respect for employee autonomy are transparency and informed consent. When collecting, storing, and using employee data, HRM should work to be as transparent as possible with workers while also obtaining their express consent and giving ways for people to view and update their information. Additionally, HRM must place a high priority on data quality and accuracy to support just and efficient decision-making. HRM can make sure that data-driven insights are trustworthy and well-informed by investing in data quality assurance methods, continuously monitoring and updating data, and quickly correcting mistakes. Finally, reducing ethical risks is greatly helped by employee empowerment and algorithmic oversight. Employees should be given the opportunity to challenge algorithmic judgements when appropriate, and HR experts should maintain control over decision-making processes. By addressing these ethical issues, HRM can support the principles of justice, trust, and respect for employee rights as well as create an ethical foundation for data analysis. 

References

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