Five Strategies for Avoiding Favouritism at Work

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Workplace favouritism threatens team cohesion, employee retention and company diversity. Below, we’ll explain how to foster inclusion, tackle favouritism at a cultural level and design fair policies and practices.

Understanding favouritism and how it can show up at work

Favouritism usually occurs in the workplace when managers treat certain employees or teams more favourably than others. While having better working relationships with certain people is natural, these relationships can become problematic when authority figures treat particular team members better for reasons unrelated to performance.

Workplace favouritism typically falls into two categories, with managers either extending greater privileges to the favoured employee or giving them more leeway than other workers. Examples include:

Praising certain employees for accomplishments more than others;
Prioritizing a person for promotion over more experienced or accomplished colleagues;
Offering greater flexibility to particular employees;
Giving certain employees a lighter workload or more enjoyable responsibilities;
Allowing an employee to get away with mistakes or behaviour not tolerated in other workers;
Extending perks to certain employees and not others;
Taking sides against particular employees in workplace disputes, and;
Only sharing privileged information with certain groups without good reason.

However, favouritism can also occur between colleagues of the same rank. For instance, a team member showing an open preference for working with certain people or covering up another person’s mistakes can cause significant cultural problems. An unequal dynamic between peer workers often leads to resentment, poor communication and even bullying or harassment when left unaddressed.

Depending on your jurisdiction, favouritism in the workplace isn’t necessarily illegal. However, it can sometimes constitute discrimination, especially if the person subjected to unfair treatment has a protected characteristic. For example, treating someone unfairly due to their race, sex, or religion constitutes a crime in many places, exposing employers to possible litigation.

Even if workplace favouritism is legal, it can have a significant, negative impact on your company culture. Favouritism often demotivates workers, potentially reducing productivity and team cohesion. Furthermore, employees may consider working elsewhere if they feel unrecognized and unrewarded for their skills and hard work.

Managerial favouritism sometimes leads to deskilling because it creates an environment where certain people’s abilities become overlooked. This culture often prevents certain employees from growing and developing within their roles, and managers may miss opportunities to leverage unique skill sets.

How to prevent favouritism in the workplace

First of all, it’s important to understand that not every example of preferential treatment constitutes favouritism. In fact, being understanding of small mistakes made by an otherwise outstanding employee or accommodating difficult life circumstances are examples of good management, provided the approach is consistent and fair.

That being said, good managers also take steps to prevent unjustifiable favouritism in order to keep team unity and motivation alive.

Creating a fair workplace culture where everyone has the chance to thrive is crucial. As a manager, you can implement systems and processes to encourage consistently equal treatment and reduce the risk of unfairly preferential treatment occurring. Whether you’re tackling existing favouritism or want to keep it out of your workplace, the following strategies can help you achieve your goal.

Implement clear and objective systems for evaluating employee performance

Performance management often becomes a thorny issue in workplaces when evaluation systems are inconsistent. To combat this, create an objective method for evaluating employees and apply it across the board. Ideally, your assessment rubric should include measurable performance objectives instead of leaving judgments open to subjective interpretation.

When designing your performance management system, ensure employees understand what they need to do to succeed. Clearly communicate responsibilities and expectations for what success looks like to ensure everyone knows how to achieve recognition for their accomplishments. Having these specific parameters in place reduces the potential for misunderstanding and bias.

Another factor to consider is that certain job roles lend themselves more easily to going the extra mile. Additionally, going above and beyond may not be possible for every employee because of family commitments, illness, or other responsibilities.

When a workplace has an unspoken expectation for employees to exceed their targets or take on additional tasks, certain people will always struggle to gain recognition. You can tackle this problem by assessing performance against each person’s job description and praising them for performing their core responsibilities well.

Some managers also like to monitor who receives praise in the workplace and how often to identify overlooked employees and rectify the situation. But this strategy doesn’t mean you have to give everyone equal recognition regardless of their performance — If a team member is underperforming, schedule a meeting to discuss the issue, offer support, and reaffirm expectations. Then, explore options for development to help them get back on track.

Promote feedback and open communication

Communication problems can make favouritism (or perceptions of bias) harder to tackle. Do your best to ensure employees feel comfortable speaking up when they feel unfairly overlooked, penalized or unrewarded so you can tackle minor issues before they snowball and cause resentment.

There are several ways to promote more open communication, and what works best depends on your team’s preferences and company structure. Some managers find gathering regular employee feedback helpful for identifying favouritism issues. You can also schedule regular check-ins with each team member to give them time to discuss their experience.

Promoting communication has several additional advantages. It creates a more transparent working environment and can help foster trust between managers and employees. Additionally, a culture where everyone feels heard reduces the risk of the wrong person taking credit for another’s good work.

Unfortunately, many workplaces allow favouritism to go unchallenged. OC Tanner’s 2023 Global Culture Report found that only 63% of employees feel appreciated at work. While that figure improved by 11% since 2022, it still indicates a widespread issue with achievement recognition.

This also tells us there’s a reasonable chance that new hires come in with negative perceptions of what to expect in terms of workplace fairness. You can provide reassurance by briefing new employees on your policies related to favouritism during their onboarding meetings and explaining how to get support if they experience unfair treatment.

Implement best practices for diversity and inclusion

Favouritism can have serious consequences for employers when it results in discrimination against someone based on their protected characteristics. A culture of favouritism can also severely undermine efforts to promote workplace equality and inclusion, making your workplace potentially inhospitable to women and minority groups. Therefore, creating systems to prevent favouritism while championing best practices for diversity and inclusion go hand in hand.

Promoting inclusion offers multiple advantages to businesses, such as improved innovation and creativity. However, simply employing a diverse workforce isn’t enough. Without a fair workplace culture, women and employees from minority groups may either leave the company or accept the culture as it is. Acceptance of unfair working practices often means employers lose valuable insights, ideas and risk-management strategies because employees feel unable to address organizational inequality.

You should consider equality and inclusion in every facet of your business practices, from recruitment and promotion to performance management. Rigorously evaluating your business to assess progress towards a fully inclusive workplace can help you identify areas for development and determine whether discriminatory favouritism is a problem.

Provide leadership training

Policies and practices to eliminate favouritism are more effective when applied. Once you’ve developed fair recruitment, performance management and communication strategies, ensure all managers know how to implement them to prevent personal bias or unaligned practices from creeping in. You may wish to provide training to help managers understand expectations around fair working processes. Including employees in training, or briefing them on the main goals of the program, is a great way to ensure they know what to expect from leaders and how to communicate issues.

You could also consider providing education to help leaders and other employees understand the risks of favouritism in the workplace and spot the signs of unjust preferential treatment. Identifying favouritism requires the right knowledge, and a lack of communication between departments can make spotting and challenging the behaviour all the more difficult. Clarifying your management team’s responsibility to speak out against favouritism reduces the risk of problematic practices flying under the radar.

Strengthen your HR department

A strong, knowledgeable HR department is crucial to ensuring fair compensation, employee education and talent management across your entire company. Therefore, making sure all HR employees understand the potential impact of favouritism on the company and know how to tackle unfair treatment and discrimination is essential. Consider providing ongoing training to strengthen your HR department and encourage regularly scheduled reviews of working practices.

Your HR department can help you design robust systems for combatting bias during recruitment, promotion and performance management cycles. These professionals can also establish and monitor employee education to ensure every team member understands their rights and responsibilities regarding workplace favouritism and receives equal opportunities for professional development.

Amid recent cultural changes that have rocked the HR landscape, it’s essential that HR professionals know how to respond to reports of favouritism in the workplace. Inappropriate responses can open employers up to accusations of workplace discrimination in some circumstances. Furthermore, failing to take reports seriously can leave employees feeling unheard, forcing them to either leave the company or grudgingly accept the status quo. When dealing with employee complaints around favouritism, HR professionals should:

Listen carefully to the employee’s concerns;
Make accurate notes;
Explore the work-related and emotional effects on the employee and their colleagues;
Consider any personal issues contributing to each party’s behaviour and perceptions;
Consider whether the conduct described constitutes favouritism, including exploring all possible explanations;
Follow workplace policies consistently to resolve issues, and;
Explore opportunities for learning by considering how the company can become a fairer workplace in response to problems raised.

Common challenges when combatting favouritism (and possible solutions)

In an ideal world, every company would have access to an expert HR department to help make workplace favouritism a thing of the past. However, this simply isn’t the reality for many businesses. If you work for a small company, you likely don’t have the same HR resources as larger companies.

But you can take steps to combat unfair practices with even limited resources. Designating the task of developing consistent policies and overseeing progress to a specific person or team can provide the leadership you need to tackle workplace favouritism head-on. Written guidance for managers and employees on handling instances of unjust bias can also offer a valuable refresher on expectations.

However, challenging unfairness at work is everyone’s responsibility. While having a leader on the initiative can help provide momentum, monitoring the workplace for bias and discrimination is too much for one person. Therefore, setting expectations for managers to promote fairness in their own teams and fostering a company-wide culture of anti-favouritism is vital for ensuring a consistent approach.

In Summary

Creating an open, inclusive company culture is an effective way to ensure employees receive fair benefits, working conditions and recognition. Furthermore, combatting favouritism in the workplace helps prevent resentment between colleagues and enhances motivation, performance, and employee retention. Managers can prevent and tackle favouritism by implementing fair, consistent policies, assessing employee performance against objective metrics and promoting diversity and inclusion.

About the Author

Emily Crowley is a Senior Writer and Career Expert at Resume Genius, where she loves helping job seekers overcome obstacles and advance their careers. She graduated from George Mason University with a degree in Foreign Language and Culture.

The post Five Strategies for Avoiding Favouritism at Work appeared first on The 6Q Blog.

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DynIBaR: Space-time view synthesis from videos of dynamic scenes

Posted by Zhengqi Li and Noah Snavely, Research Scientists, Google Research

A mobile phone’s camera is a powerful tool for capturing everyday moments. However, capturing a dynamic scene using a single camera is fundamentally limited. For instance, if we wanted to adjust the camera motion or timing of a recorded video (e.g., to freeze time while sweeping the camera around to highlight a dramatic moment), we would typically need an expensive Hollywood setup with a synchronized camera rig. Would it be possible to achieve similar effects solely from a video captured using a mobile phone’s camera, without a Hollywood budget?

In “DynIBaR: Neural Dynamic Image-Based Rendering”, a best paper honorable mention at CVPR 2023, we describe a new method that generates photorealistic free-viewpoint renderings from a single video of a complex, dynamic scene. Neural Dynamic Image-Based Rendering (DynIBaR) can be used to generate a range of video effects, such as “bullet time” effects (where time is paused and the camera is moved at a normal speed around a scene), video stabilization, depth of field, and slow motion, from a single video taken with a phone’s camera. We demonstrate that DynIBaR significantly advances video rendering of complex moving scenes, opening the door to new kinds of video editing applications. We have also released the code on the DynIBaR project page, so you can try it out yourself.

Given an in-the-wild video of a complex, dynamic scene, DynIBaR can freeze time while allowing the camera to continue to move freely through the scene.

Background

The last few years have seen tremendous progress in computer vision techniques that use neural radiance fields (NeRFs) to reconstruct and render static (non-moving) 3D scenes. However, most of the videos people capture with their mobile devices depict moving objects, such as people, pets, and cars. These moving scenes lead to a much more challenging 4D (3D + time) scene reconstruction problem that cannot be solved using standard view synthesis methods.

Standard view synthesis methods output blurry, inaccurate renderings when applied to videos of dynamic scenes.

Other recent methods tackle view synthesis for dynamic scenes using space-time neural radiance fields (i.e., Dynamic NeRFs), but such approaches still exhibit inherent limitations that prevent their application to casually captured, in-the-wild videos. In particular, they struggle to render high-quality novel views from videos featuring long time duration, uncontrolled camera paths and complex object motion.

The key pitfall is that they store a complicated, moving scene in a single data structure. In particular, they encode scenes in the weights of a multilayer perceptron (MLP) neural network. MLPs can approximate any function — in this case, a function that maps a 4D space-time point (x, y, z, t) to an RGB color and density that we can use in rendering images of a scene. However, the capacity of this MLP (defined by the number of parameters in its neural network) must increase according to the video length and scene complexity, and thus, training such models on in-the-wild videos can be computationally intractable. As a result, we get blurry, inaccurate renderings like those produced by DVS and NSFF (shown below). DynIBaR avoids creating such large scene models by adopting a different rendering paradigm.

DynIBaR (bottom row) significantly improves rendering quality compared to prior dynamic view synthesis methods (top row) for videos of complex dynamic scenes. Prior methods produce blurry renderings because they need to store the entire moving scene in an MLP data structure.

Image-based rendering (IBR)

A key insight behind DynIBaR is that we don’t actually need to store all of the scene contents in a video in a giant MLP. Instead, we directly use pixel data from nearby input video frames to render new views. DynIBaR builds on an image-based rendering (IBR) method called IBRNet that was designed for view synthesis for static scenes. IBR methods recognize that a new target view of a scene should be very similar to nearby source images, and therefore synthesize the target by dynamically selecting and warping pixels from the nearby source frames, rather than reconstructing the whole scene in advance. IBRNet, in particular, learns to blend nearby images together to recreate new views of a scene within a volumetric rendering framework.

DynIBaR: Extending IBR to complex, dynamic videos

To extend IBR to dynamic scenes, we need to take scene motion into account during rendering. Therefore, as part of reconstructing an input video, we solve for the motion of every 3D point, where we represent scene motion using a motion trajectory field encoded by an MLP. Unlike prior dynamic NeRF methods that store the entire scene appearance and geometry in an MLP, we only store motion, a signal that is more smooth and sparse, and use the input video frames to determine everything else needed to render new views.

We optimize DynIBaR for a given video by taking each input video frame, rendering rays to form a 2D image using volume rendering (as in NeRF), and comparing that rendered image to the input frame. That is, our optimized representation should be able to perfectly reconstruct the input video.

We illustrate how DynIBaR renders images of dynamic scenes. For simplicity, we show a 2D world, as seen from above. (a) A set of input source views (triangular camera frusta) observe a cube moving through the scene (animated square). Each camera is labeled with its timestamp (t-2, t-1, etc). (b) To render a view from camera at time t, DynIBaR shoots a virtual ray through each pixel (blue line), and computes colors and opacities for sample points along that ray. To compute those properties, DyniBaR projects those samples into other views via multi-view geometry, but first, we must compensate for the estimated motion of each point (dashed red line). (c) Using this estimated motion, DynIBaR moves each point in 3D to the relevant time before projecting it into the corresponding source camera, to sample colors for use in rendering. DynIBaR optimizes the motion of each scene point as part of learning how to synthesize new views of the scene.

However, reconstructing and deriving new views for a complex, moving scene is a highly ill-posed problem, since there are many solutions that can explain the input video — for instance, it might create disconnected 3D representations for each time step. Therefore, optimizing DynIBaR to reconstruct the input video alone is insufficient. To obtain high-quality results, we also introduce several other techniques, including a method called cross-time rendering. Cross-time rendering refers to the use of the state of our 4D representation at one time instant to render images from a different time instant, which encourages the 4D representation to be coherent over time. To further improve rendering fidelity, we automatically factorize the scene into two components, a static one and a dynamic one, modeled by time-invariant and time-varying scene representations respectively.

Creating video effects

DynIBaR enables various video effects. We show several examples below.

Video stabilization

We use a shaky, handheld input video to compare DynIBaR’s video stabilization performance to existing 2D video stabilization and dynamic NeRF methods, including FuSta, DIFRINT, HyperNeRF, and NSFF. We demonstrate that DynIBaR produces smoother outputs with higher rendering fidelity and fewer artifacts (e.g., flickering or blurry results). In particular, FuSta yields residual camera shake, DIFRINT produces flicker around object boundaries, and HyperNeRF and NSFF produce blurry results.

Simultaneous view synthesis and slow motion

DynIBaR can perform view synthesis in both space and time simultaneously, producing smooth 3D cinematic effects. Below, we demonstrate that DynIBaR can take video inputs and produce smooth 5X slow-motion videos rendered using novel camera paths.

Video bokeh

DynIBaR can also generate high-quality video bokeh by synthesizing videos with dynamically changing depth of field. Given an all-in-focus input video, DynIBar can generate high-quality output videos with varying out-of-focus regions that call attention to moving (e.g., the running person and dog) and static content (e.g., trees and buildings) in the scene.

Conclusion

DynIBaR is a leap forward in our ability to render complex moving scenes from new camera paths. While it currently involves per-video optimization, we envision faster versions that can be deployed on in-the-wild videos to enable new kinds of effects for consumer video editing using mobile devices.

Acknowledgements

DynIBaR is the result of a collaboration between researchers at Google Research and Cornell University. The key contributors to the work presented in this post include Zhengqi Li, Qianqian Wang, Forrester Cole, Richard Tucker, and Noah Snavely.