Fundamentals

Small business owners often wear multiple hats, juggling everything from payroll to customer service, and the promise of automation Meaning ● Automation for SMBs: Strategically using technology to streamline tasks, boost efficiency, and drive growth. whispers of relief, a chance to shed some of those burdens. Yet, when considering automation’s reach, especially into hiring, a crucial question arises ● does this technological tide lift all boats equally, or does it inadvertently create new currents that leave some behind?

Automation’s Allure for Small Businesses

For a Main Street bakery or a burgeoning tech startup, automation presents itself as a tempting solution to perennial challenges. Imagine a local café struggling with overflowing order queues during the morning rush; implementing an automated ordering system seems like a straightforward fix. This same logic extends to hiring processes.

SMBs, often operating on tight margins and leaner teams than their corporate counterparts, are perpetually seeking efficiency. Automation in hiring, promising to streamline tasks like screening resumes and scheduling interviews, appears to be a godsend.

Consider the sheer volume of applications a small business owner might sift through when posting a job opening. Manually reviewing each resume, filtering out unqualified candidates, and then painstakingly scheduling interviews consumes valuable time, time that could be spent on core business activities like product development or customer engagement. Automation tools, powered by algorithms and artificial intelligence, step in with the promise of efficiency. They can scan resumes for keywords, assess candidate qualifications against pre-set criteria, and even conduct initial screenings through chatbots or pre-recorded video interviews.

Automation, at its core, is about doing more with less, a mantra that resonates deeply within the SMB landscape.

This allure of efficiency is not merely about saving time; it’s also about reducing costs. Hiring is an expensive endeavor for any business, but for SMBs, these costs can be particularly burdensome. Every hour spent on manual screening and administrative tasks is an hour not spent generating revenue.

Automation tools, while requiring an initial investment, often promise a significant return in the long run by reducing the time and resources needed for hiring. This cost-saving potential is a powerful motivator for SMBs Meaning ● SMBs are dynamic businesses, vital to economies, characterized by agility, customer focus, and innovation. to embrace automation in their hiring practices.

The Double-Edged Sword of Efficiency

However, the pursuit of efficiency, while understandable and often necessary, can sometimes cast a shadow on other critical aspects of business, including inclusivity. When automation enters the hiring process, it introduces a layer of technology that, while seemingly objective, can inadvertently perpetuate existing biases or create new barriers to entry for diverse candidates. The algorithms that power these tools are built by humans, and humans, despite their best intentions, are prone to biases, conscious or unconscious.

For instance, consider resume screening software that relies heavily on keyword matching. While this can quickly filter out irrelevant applications, it can also inadvertently disadvantage candidates who may not use the exact keywords preferred by the algorithm, even if they possess the necessary skills and experience. This can disproportionately affect candidates from underrepresented groups who may have different educational backgrounds, career paths, or even resume writing styles. The very standardization that automation aims for can ironically become a barrier to diversity.

Furthermore, automated screening processes might inadvertently overlook candidates with non-traditional backgrounds or career trajectories. Small businesses often benefit from the fresh perspectives and diverse experiences that individuals from unconventional paths bring. If automation tools are overly rigid in their criteria, prioritizing only candidates with specific degrees or years of experience in narrowly defined roles, SMBs risk missing out on a wealth of talent from individuals who may not fit the traditional mold but possess valuable skills and unique perspectives.

Unpacking Inclusive Hiring

Before we can fully understand how automation impacts inclusive hiring, we must first define what inclusive hiring truly means within the SMB context. Inclusive hiring goes beyond simply meeting diversity Meaning ● Diversity in SMBs means strategically leveraging varied perspectives for innovation and ethical growth. quotas or ticking boxes. It is about creating a hiring process that is fair, equitable, and accessible to all qualified candidates, regardless of their background, identity, or circumstances. It is about actively seeking out and valuing diverse perspectives and experiences, recognizing that a diverse workforce is not only ethically sound but also strategically advantageous for business success.

For SMBs, inclusive hiring can take many forms. It might involve actively recruiting from diverse talent pools, such as community organizations or minority-serving institutions. It could mean reviewing job descriptions to eliminate biased language or requirements that might unintentionally deter certain groups from applying.

It could also involve providing accommodations during the hiring process to ensure that candidates with disabilities have an equal opportunity to demonstrate their abilities. In essence, inclusive hiring is about dismantling barriers and creating pathways for a wider range of individuals to join the SMB workforce.

The benefits of inclusive hiring for SMBs are manifold. Diverse teams are often more innovative and creative, bringing a wider range of perspectives to problem-solving and decision-making. They are also better equipped to understand and serve diverse customer bases, which is increasingly important in today’s globalized and interconnected marketplace. Moreover, inclusive hiring can enhance a company’s reputation and brand image, attracting both top talent and socially conscious customers who value diversity and inclusion.

The Intersection ● Automation and Inclusion

The crucial question then becomes ● how does the drive for automation in SMB hiring intersect with the principles of inclusive hiring? Can automation be a tool for promoting inclusivity, or does it inherently pose a threat to diversity? The answer, like most things in business, is complex and depends heavily on how automation is implemented and utilized.

Automation, if implemented thoughtfully and ethically, has the potential to remove some of the human biases that can creep into traditional hiring processes. For example, anonymizing resumes before screening can help reduce unconscious bias based on names or demographic information. Standardized interview questions, delivered through automated platforms, can ensure that all candidates are evaluated on the same criteria, reducing the potential for subjective biases to influence hiring decisions. In these ways, automation can act as a check against human fallibility and promote a more objective evaluation process.

However, the risks are equally significant. If automation tools are not carefully designed and monitored, they can easily perpetuate and even amplify existing biases. Algorithms trained on historical data that reflects societal biases can inadvertently learn and replicate those biases in their screening and selection processes. For example, if past hiring data predominantly features candidates from a certain demographic group, an algorithm might learn to favor similar candidates in the future, even if those characteristics are not directly relevant to job performance.

Furthermore, over-reliance on automation can dehumanize the hiring process, reducing candidates to data points and overlooking the nuances of individual skills, experiences, and potential. Inclusive hiring is fundamentally about recognizing the value of human diversity and creating opportunities for individuals from all backgrounds to contribute their unique talents. If automation prioritizes efficiency and standardization at the expense of human connection and individual assessment, it can undermine the very goals of inclusive hiring.

The challenge for SMBs is to harness the power of automation for efficiency without sacrificing the principles of inclusivity.

Navigating this intersection requires a conscious and proactive approach. SMB owners need to understand both the potential benefits and the potential pitfalls of automation in hiring. They need to critically evaluate the tools they are considering implementing, ensuring that these tools are designed and used in a way that promotes fairness and equity, rather than inadvertently hindering diversity.

This involves asking tough questions about the algorithms behind these tools, the data they are trained on, and the potential for unintended consequences. It also requires a commitment to ongoing monitoring and evaluation to ensure that automation is serving the goals of inclusive hiring, not undermining them.

Practical Steps for SMBs

For SMB owners looking to embrace automation in hiring while remaining committed to inclusive practices, several practical steps can be taken. First and foremost, it is crucial to understand the limitations of automation. No tool is perfect, and no algorithm is entirely unbiased.

Automation should be seen as a tool to augment human decision-making, not replace it entirely. The final hiring decisions should always involve human judgment and consideration of the broader context beyond what algorithms can assess.

Secondly, SMBs should prioritize transparency Meaning ● Operating openly and honestly to build trust and drive sustainable SMB growth. and explainability when using automation tools. Candidates should be informed about how automation is being used in the hiring process and given the opportunity to understand how their applications are being evaluated. This transparency builds trust and ensures that candidates feel they are being treated fairly. Furthermore, SMBs should seek out automation tools that offer insights into their algorithms and data sources, allowing them to identify and mitigate potential biases.

Thirdly, SMBs should actively audit their automated hiring processes for bias. This involves regularly reviewing hiring data to identify any disparities in outcomes for different demographic groups. If biases are detected, SMBs should take corrective action, which might involve adjusting the parameters of their automation tools, retraining algorithms, or even reconsidering the use of certain tools altogether. Continuous monitoring and evaluation are essential to ensure that automation is aligned with inclusive hiring goals.

Fourthly, SMBs should focus on using automation to broaden their talent pool, rather than narrow it. Automation can be used to reach out to diverse communities and attract candidates who might not otherwise have applied. For example, automated job posting platforms can be configured to target specific demographics or geographic locations.

Social media and online advertising, when used strategically, can also help SMBs connect with diverse talent pools. The key is to use automation to expand reach and create more opportunities for diverse candidates to enter the hiring pipeline.

Finally, SMBs should remember that inclusive hiring is not just about technology; it is about culture and values. Automation is simply a tool, and like any tool, it can be used for good or ill. The ultimate success of inclusive hiring depends on a genuine commitment from the SMB owner and leadership team to create a workplace where everyone feels valued, respected, and has an equal opportunity to succeed. Automation can support this goal, but it cannot replace the human element of empathy, understanding, and a genuine desire for inclusivity.

Automation in SMB hiring is not inherently good or bad for inclusive practices. Its impact depends entirely on how it is approached and implemented. By understanding the potential pitfalls, taking proactive steps to mitigate biases, and focusing on using automation to broaden opportunities, SMBs can harness the power of technology to create more efficient and, crucially, more inclusive hiring processes. The future of SMB hiring lies in finding the right balance between technological efficiency and human values, ensuring that the pursuit of automation does not come at the expense of diversity and inclusion.

Navigating Automation Inclusive Hiring Midsize Businesses

Beyond the initial allure of streamlined processes, mid-sized businesses venturing deeper into automation for hiring encounter a more complex landscape. The rudimentary applications of automation, such as basic resume screening, quickly give way to sophisticated AI-driven tools promising to revolutionize talent acquisition. However, with this advancement comes a heightened responsibility to ensure that these technologies are not inadvertently undermining the very principles of inclusive hiring that businesses are striving to uphold.

Evolving Automation ● From Efficiency to Prediction

For mid-sized businesses, the initial phase of automation adoption often focuses on solving immediate pain points. Automated applicant tracking systems (ATS) become commonplace, digitizing and centralizing the application process. These systems offer basic functionalities like automated resume parsing, keyword-based screening, and interview scheduling.

The primary driver at this stage is efficiency ● reducing administrative burden and speeding up the hiring cycle. The impact on inclusive hiring at this level is often indirect, primarily through the standardization of processes, which can theoretically reduce some forms of overt bias.

However, as businesses mature in their automation journey, they begin to explore more advanced capabilities. AI-powered tools enter the scene, promising predictive analytics, sentiment analysis, and even automated candidate assessments. These technologies claim to go beyond simple efficiency, offering insights into candidate potential, cultural fit, and even future performance.

The promise is compelling ● to not only streamline hiring but also to improve the quality of hires and reduce costly mis-hires. This is where the intersection with inclusive hiring becomes significantly more intricate.

The shift from basic automation to AI-driven predictive tools marks a critical juncture for inclusive hiring in mid-sized businesses.

Predictive hiring tools often rely on machine learning algorithms trained on vast datasets of historical hiring data and employee performance metrics. The underlying assumption is that past success can predict future outcomes. However, this assumption is fraught with potential pitfalls when it comes to inclusive hiring.

If the historical data reflects existing biases in hiring practices ● for example, if certain demographic groups have been historically underrepresented in leadership positions ● the algorithm may inadvertently learn to perpetuate these biases. It might identify patterns and correlations that are statistically significant but ethically problematic, leading to discriminatory outcomes.

The Algorithmic Bias Paradox

The paradox of algorithmic bias Meaning ● Algorithmic bias in SMBs: unfair outcomes from automated systems due to flawed data or design. lies in the fact that these tools are often perceived as objective and data-driven, masking the subjective human biases that can be embedded within the data they are trained on. Decision-makers may place undue trust in the outputs of these algorithms, assuming that they are inherently fair and unbiased simply because they are generated by machines. This can lead to a false sense of security and a reduced scrutiny of the hiring process, potentially exacerbating existing inequalities.

Consider, for example, an AI-powered tool that analyzes candidate video interviews to assess personality traits and cultural fit. The algorithm might be trained on data that associates certain speech patterns, accents, or even facial expressions with positive or negative attributes. These associations, even if statistically correlated with past performance in a specific context, can be deeply culturally biased and discriminatory. Candidates from certain cultural backgrounds might be unfairly penalized due to subtle linguistic or non-verbal cues that are misinterpreted by the algorithm.

Furthermore, the concept of “cultural fit” itself, often touted as a key metric in automated hiring tools, can be inherently problematic from an inclusivity perspective. While it is important for employees to align with the values and mission of a company, an overemphasis on cultural fit can lead to homogeneity and stifle diversity of thought. Algorithms designed to assess cultural fit might inadvertently favor candidates who are similar to the existing workforce, perpetuating a lack of diversity and hindering innovation. A truly inclusive approach recognizes that diversity of perspectives and backgrounds is a strength, not a weakness, and seeks to build teams that are enriched by different viewpoints.

Strategic Integration ● Balancing Automation and Inclusion

For mid-sized businesses committed to inclusive hiring, the strategic integration of automation requires a careful balancing act. It is not about rejecting automation altogether, but rather about adopting a critical and informed approach. The key is to leverage automation to enhance efficiency and objectivity where appropriate, while maintaining human oversight and judgment to ensure fairness and inclusivity.

One crucial step is to prioritize transparency and auditability in the selection of automation tools. Businesses should ask vendors detailed questions about the algorithms behind their products, the data they are trained on, and the measures taken to mitigate bias. They should seek tools that offer transparency into their decision-making processes, allowing for human review and intervention when necessary. Black-box algorithms that provide outputs without clear explanations should be approached with caution, especially in sensitive areas like hiring.

Another important strategy is to diversify the data used to train AI algorithms. If historical hiring data is biased, businesses should actively seek to supplement it with data from more diverse sources. This might involve incorporating data from successful employees from underrepresented groups, or even using synthetic data to balance out existing biases in the training dataset. The goal is to create algorithms that are trained on a more representative and equitable dataset, reducing the risk of perpetuating historical inequalities.

Moreover, mid-sized businesses should focus on using automation to augment, rather than replace, human decision-making in hiring. Automation tools can be valuable for tasks like initial screening, scheduling, and data analysis, freeing up human recruiters to focus on more nuanced aspects of candidate evaluation, such as assessing soft skills, cultural fit (in a truly inclusive sense), and potential for growth. The final hiring decisions should always be made by humans, taking into account a holistic view of the candidate beyond what algorithms can capture.

Furthermore, a robust diversity and inclusion Meaning ● Diversity & Inclusion for SMBs: Strategic imperative for agility, innovation, and long-term resilience in a diverse world. strategy should be integrated into the entire automation implementation process. This includes setting clear diversity goals for hiring, training hiring managers on inclusive hiring practices, and establishing mechanisms for monitoring and evaluating the impact of automation on diversity outcomes. Automation should be seen as a tool to support, not supplant, a broader commitment to diversity and inclusion. This requires a cultural shift within the organization, where inclusivity is not just a policy but a core value that guides all business decisions, including the adoption and use of technology.

Metrics and Monitoring ● Ensuring Accountability

To ensure that automation is contributing to, rather than hindering, inclusive hiring, mid-sized businesses need to establish clear metrics and monitoring mechanisms. This involves tracking diversity data throughout the hiring process, from application to offer, and analyzing the impact of automation at each stage. Key metrics might include the diversity of applicant pools, the representation of different demographic groups at each stage of the selection process, and the diversity of hires made through automated versus traditional methods.

Regular audits of automated hiring processes are essential to identify and address any unintended biases or discriminatory outcomes. These audits should not only focus on quantitative data but also incorporate qualitative feedback from candidates and hiring managers. Candidate surveys can provide valuable insights into their experiences with automated hiring tools, highlighting any potential barriers or biases they may have encountered. Hiring manager feedback can help assess the effectiveness of automation in supporting inclusive hiring goals and identify areas for improvement.

Accountability is also crucial. Clear responsibility should be assigned for ensuring that automation is used in an ethical and inclusive manner. This might involve establishing a dedicated diversity and inclusion team or assigning responsibility to existing HR or technology leaders. Regular reporting on diversity metrics and automation outcomes should be provided to senior management and the board of directors, demonstrating a commitment to transparency and accountability at all levels of the organization.

Mid-sized businesses stand at a critical juncture in the evolution of automation and inclusive hiring. They have the opportunity to leverage advanced technologies to enhance both efficiency and equity in their talent acquisition Meaning ● Talent Acquisition, within the SMB landscape, signifies a strategic, integrated approach to identifying, attracting, assessing, and hiring individuals whose skills and cultural values align with the company's current and future operational needs. processes. However, this requires a strategic, informed, and ethically grounded approach.

By prioritizing transparency, mitigating algorithmic bias, maintaining human oversight, and establishing robust monitoring mechanisms, mid-sized businesses can navigate the complexities of automation and ensure that their pursuit of efficiency does not come at the expense of their commitment to building diverse and inclusive workforces. The future of talent acquisition in the mid-sized business sector hinges on the ability to harness the power of technology responsibly and ethically, creating a hiring landscape that is both efficient and equitable for all.

Table 1 ● Automation Stages and Inclusive Hiring Impact in SMBs

Algorithmic Equity Strategic Imperative Automation Driven SMB Growth

For sophisticated SMBs, particularly those experiencing rapid growth Meaning ● Growth for SMBs is the sustainable amplification of value through strategic adaptation and capability enhancement in a dynamic market. or operating in highly competitive talent markets, automation transcends mere operational efficiency; it becomes a strategic imperative intricately linked to long-term scalability and competitive advantage. However, the uncritical adoption of advanced automation technologies in hiring presents a paradoxical risk ● the very tools designed to propel growth may inadvertently undermine the foundational principles of inclusive hiring, thereby limiting access to diverse talent pools crucial for sustained innovation and market responsiveness.

Beyond Optimization ● Automation as Strategic Differentiation

At the advanced stage of automation maturity, SMBs are no longer simply seeking to optimize existing hiring processes. They are leveraging automation to fundamentally transform their talent acquisition strategies, aiming for a level of sophistication and agility previously unattainable. This involves integrating automation across the entire talent lifecycle, from proactive talent pipelining and personalized candidate engagement to AI-driven performance management and predictive attrition modeling. The goal is to create a data-rich, continuously learning talent ecosystem that fuels business growth and adaptability.

In this context, automation is not viewed as a cost-saving measure alone, but as a strategic differentiator. SMBs are investing in cutting-edge technologies like natural language processing (NLP) for sophisticated candidate sourcing, machine vision for automated video interview analysis, and even gamified assessments to evaluate cognitive abilities and personality traits. These tools promise to unlock deeper insights into candidate potential, predict future performance with greater accuracy, and personalize the candidate experience to attract top talent in a fiercely competitive market. The ambition is to build a hiring engine that is not only efficient but also predictive, proactive, and highly attuned to the evolving needs of the business.

Advanced automation, when strategically deployed, positions SMBs to compete for talent on a level playing field with larger corporations.

However, this pursuit of strategic differentiation through automation introduces a new layer of complexity to the inclusive hiring equation. The more sophisticated and data-driven the automation tools become, the greater the potential for subtle, yet systemic, biases to creep into the hiring process. Algorithms trained on complex datasets and designed to identify nuanced patterns may inadvertently amplify existing societal inequalities or create new forms of algorithmic discrimination that are difficult to detect and mitigate. The very sophistication that promises strategic advantage also carries the risk of entrenching bias at scale.

The Challenge of Algorithmic Equity

Algorithmic equity emerges as a paramount concern for advanced SMBs seeking to reconcile their automation ambitions with their commitment to inclusive hiring. Algorithmic equity Meaning ● Algorithmic Equity for SMBs: Ensuring fair, unbiased automated systems to foster inclusive growth and ethical operations. goes beyond simply mitigating bias in individual algorithms; it requires a holistic approach to ensuring fairness and equity across the entire automated hiring ecosystem. This involves addressing bias at multiple levels ● in the data used to train algorithms, in the design and implementation of the algorithms themselves, and in the organizational processes and governance structures that oversee the use of automation in hiring.

One of the most significant challenges lies in the inherent limitations of current AI technologies in understanding and accounting for the complexities of human diversity. Algorithms, even the most advanced ones, are fundamentally pattern-recognition machines. They excel at identifying correlations and making predictions based on historical data, but they lack the contextual understanding, empathy, and nuanced judgment that are essential for truly equitable human decision-making. Over-reliance on algorithmic outputs without critical human oversight can lead to a reductionist view of candidates, overlooking the richness and complexity of their individual experiences and potential.

Consider the use of AI in assessing candidate “potential,” a metric increasingly sought after by growth-oriented SMBs. Algorithms designed to predict potential often rely on proxies like past academic achievements, previous job titles, or network connections. These proxies, while statistically correlated with career success in certain contexts, can be deeply biased against candidates from underrepresented backgrounds who may have faced systemic barriers to accessing traditional pathways to success. An algorithm trained to equate “potential” with these biased proxies may systematically undervalue candidates who possess genuine talent and drive but lack the privileged backgrounds favored by the algorithm.

Strategic Framework for Algorithmic Equity

Addressing the challenge of algorithmic equity requires a strategic framework that integrates ethical considerations and diversity principles into every stage of the automation lifecycle. This framework should encompass several key components:

1. Ethical Algorithm Design and Development ● This involves embedding ethical principles into the very design of automation tools. It requires close collaboration between data scientists, HR professionals, and ethicists to identify and mitigate potential sources of bias in algorithms.

This includes using fairness-aware machine learning techniques, diversifying training datasets, and rigorously testing algorithms for discriminatory outcomes across different demographic groups. Transparency and explainability should be prioritized, allowing for human auditability and intervention.

2. Bias Auditing and Mitigation Protocols ● Regular and rigorous audits of automated hiring processes are essential to detect and address algorithmic bias. These audits should go beyond simple demographic parity metrics and delve into more nuanced measures of fairness, such as equal opportunity and predictive parity.

Mitigation protocols should be established to address identified biases, which may involve retraining algorithms, adjusting algorithm parameters, or implementing human overrides in specific cases. Continuous monitoring and iterative improvement are crucial.

3. Human-In-The-Loop Governance and Oversight ● Automation should augment, not replace, human judgment in hiring. A robust governance framework is needed to ensure that humans remain in the loop at critical decision points, especially in areas where algorithmic bias is a concern.

This framework should define clear roles and responsibilities for human oversight, establish protocols for human intervention and override, and provide training to hiring managers on how to interpret and contextualize algorithmic outputs in an equitable manner. Human judgment should be informed by, but not dictated by, algorithmic recommendations.

4. Data Privacy Meaning ● Data privacy for SMBs is the responsible handling of personal data to build trust and enable sustainable business growth. and Candidate Rights ● As automation becomes more data-intensive, protecting candidate data privacy and upholding candidate rights becomes paramount. SMBs must comply with relevant data privacy regulations and implement robust data security measures to safeguard candidate information.

Candidates should be informed about how their data is being collected, used, and stored, and they should have the right to access, rectify, and erase their data. Transparency and consent are essential for building trust and maintaining ethical data practices.

5. Continuous Learning and Adaptation ● The landscape of automation and inclusive hiring is constantly evolving. SMBs must adopt a mindset of continuous learning and adaptation, staying abreast of the latest research and best practices in algorithmic equity and diversity and inclusion.

This involves investing in ongoing training for HR professionals and data scientists, participating in industry forums and collaborations, and actively seeking feedback from diverse stakeholders. A commitment to continuous improvement is essential for navigating the complexities of this evolving field.

Competitive Advantage Through Algorithmic Equity

For advanced SMBs, algorithmic equity is not merely a matter of ethical compliance; it is a strategic imperative that can unlock significant competitive advantages. By building hiring processes that are both efficient and equitable, SMBs can tap into wider and more diverse talent pools, fostering innovation, creativity, and adaptability. A reputation for fairness and inclusivity can also enhance employer branding, attracting top talent who increasingly prioritize these values. In a competitive talent market, algorithmic equity can be a powerful differentiator, giving SMBs an edge in attracting and retaining the best and brightest.

Conversely, neglecting algorithmic equity carries significant risks. Biased automation systems can perpetuate and amplify existing inequalities, leading to homogeneous workforces, reduced innovation, and reputational damage. In an increasingly diverse and socially conscious marketplace, SMBs that fail to prioritize inclusive hiring risk alienating customers, investors, and employees alike. Algorithmic bias can become a liability, undermining both business performance and brand reputation.

The future of SMB growth in the automation-driven era hinges on the ability to harness the power of technology responsibly and ethically. For advanced SMBs, this means embracing algorithmic equity as a core strategic principle, integrating it into their talent acquisition strategies, and building a culture of inclusivity that permeates every aspect of their operations. By prioritizing both efficiency and equity, these SMBs can unlock the full potential of automation to drive sustainable growth, innovation, and long-term success in an increasingly complex and competitive business landscape.

Table 2 ● Strategic Framework for Algorithmic Equity in SMB Hiring

References

• O’Neil, Cathy. Weapons of Math Destruction ● How Big Data Increases Inequality and Threatens Democracy. Crown, 2016.
• Noble, Safiya Umoja. Algorithms of Oppression ● How Search Engines Reinforce Racism. NYU Press, 2018.
• Eubanks, Virginia. Automating Inequality ● How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press, 2018.