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Race and Gender Bias at Google

Race and Gender Bias at Google

Introduction

Current case study focuses on the analysis of race and gender bias in one of the world’s most influential companies, Google. However, it is worth noting that at Google racism does not have outrageous external features as it had, for example, in the times of Jim Crow laws. It is a new type of racism that functions on a different level than earlier. As Bonilla-Silva (2010) argues, “Despite the ebb and flow of racial violence in the new racism period… the racial domination is still fundamentally maintained through new practices – economic, political, social and ideological.” The same statement can be true about discrimination of women at the workplace. The situation has drastically improved since the time when women did not have a right to vote, but acquired new forms and realizations. Such forms of unethical behavior will be analyzed not on the basis of a small company, but a large corporation that is considered to be a flagship of global technological development. Such fact makes the case of race and gender bias at Google even more demonstrative and representative. Google declares equal opportunities for all employees and even uses its famous unofficial slogan “Don’t be evil.” Nevertheless, the cases of bias in the process of choosing employees are not uncommon.

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Pertinent Features of the Case

According to the latest reports made by Google, there are no signs of equality between employees. For example, black employees are represented only with 2% of the whole staff. Whites occupy 61% of the staff in overall and more than 72% in the leadership. In addition, women are underrepresented at Goggle. In general, only 30% of Google staff is represented by female. The situation is even worse in leadership (21% of women) and technical jobs (17% of women) in different departments and venues (Jacobson, 2015).

Such bias has impact not only on the underrepresentation of non-whites and women among Google’s staff. It is expressed even at more subtle levels. For example, “the company opened a new building, and someone spotted the fact that all the conference rooms were named after male scientists” (Manjoo, 2014). Of course, the names of the rooms do not directly affect the lives of the employees or deprive them of certain benefits, but such facts perfectly reflect the situation with discrimination at Google.

The Ethical Problem

The important aspect of this case is its duality that is based on the official standards of the corporation and the real situation with ethics. The ethical code of Google manifests the absolute intolerance towards any type of discrimination. However, the above-mentioned data shows the disparity between declarations and actual state of personal issues. The problem at Google arises when race and gender bias intervenes in the way candidates and employees are treated. The distribution of benefits in this case is not equal. Moreover, it is not based on the professional qualities of the individuals. “Gender stereotypes and the expectations they produce about both what women are like and how they should behave can result in devaluation of their performance, denial of credit to them for their successes” (Heilman, 2001). In addition, the representatives of non-white ethnicities cannot receive proper evaluation as specialists, as their race is taken into account while selecting employees for the vacancies. Such case shows that the ethical code at Google is inefficient. McMillan (2012) offers an apt metaphor to describe this situation “having a code of ethics without creating an ethical culture and a comprehensive ethics program is like having a Ferrari without wheels – i.e., it looks good, but you’re not going anywhere.” It is obvious that Google does not pay enough attention to the actual implementation of the code and the creation of the comprehensive ethic culture that would embrace all the levels of the corporation.

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The Alternatives

Google is not entirely passive about changing the inadequate representation of non-whites and women. The corporation makes some positive steps. For example, they argue that “the hiring of African-Americans and Latinos in both technical and non-technical roles has outpaced the overall hiring in those roles” (Kokalitcheva, 2015). They also try to establish links with universities for hiring the underrepresented social groups. However, the rate of progress is very low. Google should have paid more attention to the fact that it deals with “new racism” and the bias they are trying to eliminate is hidden (Manjoo, 2014). Such subconscious discrimination is significantly more difficult to tackle than the outspoken racism or women discrimination. It would be an efficient step to include into the seminars about this issue more psychological information to help employees (especially those in the HR departments) understand that their actions are indeed influenced by their subconscious beliefs. Moreover, the corporation should focus on the links between different measures that they make in their attempts to eliminate the bias. They should not work separately as it is not likely to bring sufficient results.

In addition, Google should exclude “profit” aspects from reasons why the staff should follow the code of conduct. For example, Laszlo Bock, Google’s executive in charge of human resources, argued that ethical behavior and workforce diversity could have a beneficial effect on the business aspects (Manjoo, 2014). Such approach is not supposed to have a positive impact on the ethical environment (Nyberg, 2007).

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Rationale for the Chosen Course of Action

The chosen course of action should bring positive results and make a significant contribution to the process of eliminating hidden race and gender bias at Google. In addition, the offered measures take into account the complex nature of subconscious discrimination. It cannot be solved with straightforward lectures or directions from the management. The alternative system offered above has more potential in solving the problem of bias as it makes use of the integrated psychological approach to the racism and gender discrimination in the frames of new hidden bias. Moreover, it also pays enough attention to the actual implementation of ethical code and specific measures that would improve the ethic culture at Google. Moreover, it would clear state moral reasons for behaving ethically and stop focusing on the business aspects of this code. Such reasoning is supposed to have more powerful impact on the staff as the discussed bias is caused by the subconscious process and not logic decisions.

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Conclusion

The case of race and gender bias at Google is very complex. Despite the official ban on unethical behavior that includes discrimination on ethnic and gender terms, the existence of ethical code of conduct and many other aspects that should have made the bias minimal or perhaps even eliminate it completely, the situation with the representation of women and non-whites at Google cannot be considered satisfactory. In this particular case study the hidden bias at Google hinders equal distribution of benefits among all members of the society. The fact that Google management is trying to eliminate this bias can be considered positive, but low efficiency of these measures should accelerate the search for other methods that would work in this environment and under these conditions. Therefore, the alternative system of measures offered above is supposed to cover the aspects of this problem that were not addressed by the current anti-bias program at Google.

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