Building An Ethical Organisation: Boost Morale & Consumer Trust

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Not too long ago, most companies cared only about the bottom line – sales, revenue, profit margins – and not so much about ethics. But now, as the business landscape evolves, being an ethical organisation and being successful goes in tandem.

To succeed as an organisation, you need to have ethical structures in place. The only way to do that is to completely overhaul your current corporate culture and transform it into one with ethics embedded into every facet – from the employees and customers to the product(s) and manufacturing process.

Read on to find out what an ethical (and unethical) organisation is, the characteristics of an ethical organisation, how to build an ethical culture, and the importance of ethics in the workplace.

What is an ethical organisation?

An ethical organisation is one with set principles that govern the company’s operations, the interactions between the company’s stakeholders (management, employees, and customers), and the quality of its products and/or services.

It’s easy to think an ethical culture is one in which people simply obey applicable rules and regulations. But the reality is a bit more complex. Being ethical isn’t only about following rules; it’s about creating an environment in which employees are able to openly discuss “grey area” situations and consider the impact of their decisions on the company, their colleagues, their customers, and the communities they’re trying to serve.

Ethical organisations also obey the law in matters concerning how they treat employees, customers, and even their competitors. Here are some of these laws:

Children under the age of fourteen years shouldn’t be employed to work in any organisation.
Organisations must not discriminate against employees and customers on the basis of physical appearance, sex/gender, age, nationality, social status, or disability.
Employees shouldn’t share their company’s confidential information with external parties, including friends and family.
Organisations must not exploit their employees’ skills or refuse to pay them their dues (arrears, salary, incentives, bonuses, and other reimbursements) in a timely manner.
Organisations must prioritise the physical safety of their employees and not expose them to hazardous situations.

What makes an organisation unethical?

An unethical organisation is one that engages in practices that don’t meet the minimum standards for a business code of conduct. These practices are often morally wrong and can harm people, animals, and/or the environment.

Here are some examples of unethical behaviour in the workplace:

Sharing confidential information with competitors

Confidential information in a business can range from monetary transactions to client databases to product recipes. These things are integral to the growth of a business and the protection of its customers, so sharing them with external parties (especially competitors) is an unethical practice.

Making employees who know confidential information sign an NDA or a confidentiality agreement can be a way to prevent the leak of sensitive information, but still, not all companies apply this with their workforce.

Manipulating financial statements

Some companies who want to get more investors, lenders, and consumers tweak their financial reports to show a better financial performance than they actually have – overvalued inventory, inflated profits, and reduced depreciation.

Cooking the books makes a business seem profitable, so investors buy more stocks from the share market – which they oftentimes lose because the company tricked them. This is not only unethical, but it’s a crime punishable by law.

For example, in 2011, Michael Woodford, the British chief executive at camera-making corporation Olympus, exposed a $1.7bn fraud at the company. Turns out Olympus had been using acquisitions to cover up losses on bad investments since the 1990s.

The company’s former chairman Tsuyoshi Kikukawa and two other executives got suspended prison sentences, while one of the company’s advisers went to jail for four years.

Creating unfair competition

While there’s nothing wrong with using your competitor’s name to promote your company, it’s unethical to attack competitors or make unjust claims about them without facts to back up your argument.

Defaming a competitor, infringing on their trademarks, and misappropriating their trade secrets all fall under creating unfair competition, and they give consumers the wrong impression about the competitor.

A famous example of this is when PepsiCo sued Coca-Cola for “false advertising and taking scientific liberties” when Coca-Cola claimed its energy drink, Powerade, was “superior” to PepsiCo’s Gatorade, which it termed as an “incomplete sports drink”.

Mistreating employees

It’s not uncommon to hear of companies who make their employees do tasks outside the scope of their job or work long hours without paying them for overtime. Some companies also deliberately seek cheap labour, and when they find it, they put those employees through stressful physical and mental conditions for less than minimum wage.

This is unethical behaviour.

Foxconn, a Taiwanese manufacturing company, is a major manufacturer of Apple products, including the expensive iPhone. People have accused this company several times of employing child workers who work in horrible situations for up to 70 hours a week.

Suicide notes and survivors speak of the long workdays, extreme stress in the workplace, and barbarous managers who make false promises of benefits and humiliate workers for mistakes.

Using customer data inappropriately

All businesses collect and store customer data in different ways. For example, social media platforms collect information like your full name, home address, phone number, and email address to better understand your preferences, personalise your experience, and optimise their marketing campaigns.

However, some businesses share (or sell) their customers’ data with third parties – which leads to data misuse and breaches. This, in turn, leads to a bad reputation and expensive lawsuits.

In 2018, news outlets revealed that Cambridge Analytica, a UK political consulting firm, acquired and misused the personal data of nearly 87 million Facebook users, most of whom didn’t give the company explicit permission for the company to access or use their information.

Less than two months after the news broke out, Cambridge Analytica went bankrupt and defunct, and the Federal Trade Commission (FTC) ordered Facebook to pay a $5 billion fine.

The characteristics of an ethical organisation

While all organisations are different, there are five elements all ethical companies exhibit. These elements are:

Respect

In an ethical organisation, everyone – from the C-suite to low-level employees – shows respect. Each stakeholder understands that the business cannot thrive through one person’s efforts alone, so they acknowledge the contributions of their colleagues, customers, and vendors.

Showing respect helps inspire loyalty, improves the brand’s reputation, boosts productivity, and makes the staff feel safe, as proven in SHRM’s 2022 Global Culture Research Report.

Integrity

Integrity is as simple as the basic rules we learn as kids – don’t lie, steal, or cheat other people. An ethical business takes these rules to heart in the way employees interact with customers and with one another.

When anyone doesn’t follow these rules employees report it, and the company’s leadership thoroughly investigates the misconduct and punishes the wrongdoer(s) appropriately.

Prioritising the wellbeing of employees, customers, and community

An ethical organisation knows it wouldn’t exist without the help of its employees and customers, so it endeavours to treat them well and give back to the groups that made it successful.

This means making the workplace physically safe, paying employees a good wage, allowing workers to care for their physical and mental health, running the business in an eco-friendly way, and sponsoring local events, among other things.

Obedience to company rules and regulations

In an ethical organisation, employees understand why rules exist and they do their best to comply with those rules/policies. They read through the organisation’s rulebook during their onboarding and ensure their actions align with company guidelines.

Compassion

The actions and decisions of employees in an ethical organisation are almost always predicated on kindness. As such, an ethical company takes a strong stance against mistreatment of employees, sexual harassment, bigotry/discrimination of any kind, and exploitation.

Image: Pexels

Ways to build an ethical culture within your organisation

Building an ethical culture within an organisation is a herculean task. It goes beyond setting a bunch of rules for people to follow; it’s about changing the behaviours and mindsets of employees (and leadership) to focus more on the interests and well-being of colleagues and customers, over their own personal gain.

Below are some steps you can take to transform the culture of your organisation for the better:

Create a code of conduct

A code of conduct is a set of rules, principles, values, and standards outlining what leadership expects from employees within the organisation. This covers everything from work attire and interpersonal relationships to concerns about human rights and social responsibility.

Developing a code of conduct and sharing it with all employees in the organisation puts everyone on the same page as to what to wear, how to behave, how to make ethical decisions that benefit the entire organisation, and what to do when they reach an impasse.

As you work with your HR and/or ethics department to make your company’s code of conduct, let your employees know the document won’t be something that simply sits on a shelf. Instead, it’ll be an integral part of how you conduct business internally and externally.

Pro tip: You should review and update your code of conduct every one or two years to ensure your organisation remains as ethical as possible.

Model desired behaviour

It’s not enough for a company’s leaders to simply write down how they expect employees to behave; they should also model these desired behaviours themselves.

A 2020 research report by Chen et al. shows employees who thrive in the workplace are often influenced by their leaders’ positive behaviours. As such, organisational leaders must understand employees are watching them closely, and practising what they preach is a more practical approach to creating an ethical work culture than simply telling employees what to do and how to act.

Include ethics programs in the onboarding process

Adding a short ethics training program to the employee onboarding process can help you ensure new hires resume their roles knowing exactly how the company operates when it comes to ethical behaviours and decision-making.

These training programs should not only explain the company’s code of conduct, but they should also give new hires the strategies and tools they need to adhere to ethical behaviours and solve dilemmas.

Hold regular meetings about culture in the workplace

Workplace ethics are not something you relay to employees once and forget about; they need occasional reinforcement. That’s why you should organise (bi)annual meetings with your employees to review the organisation’s ethics.

At these meetings, all employees, no matter their position, should feel free to express any concerns they have with the way things are done in the company. This way, you’ll find out about any ethical issues in the workplace and make changes, if necessary.

Punish ethical violations appropriately

It’s not enough to talk about ethics and pass around an employee rulebook; you should also be willing to meticulously investigate any ethical violations and punish the offender appropriately. Your code of ethics should clearly outline the consequences of unethical behaviour, and leadership must be willing to enforce these guidelines.

Don’t be an organisation that sweeps bad behaviour and ethical violations under the rug. If you do, you’ll develop a bad reputation internally – which will become external in no time.

How to measure the ethical quality of an organisation

Ethics seem like a fuzzy element you can’t measure in a company. But you can – and you should.

Measuring company ethics can give you insights into the level of integrity your workforce – and organisation, at large – has. And if you already have objective measures and training programs in place to boost ethical behaviour, you’ll be able to monitor how effective those measures and programs are.

Here are a few ways to measure the ethical quality of your organisation:

Employee and customer surveys;
Awards, honours, or recognitions your company has received related to its corporate responsibility, work culture, ethics, and integrity;
Employees who regularly attend ethics training sessions versus those who miss it;
The level of complaints your customers make regarding honesty, transparency, and discrimination;
Personal interviews with each employee to gauge communication, interpersonal, and problem-solving skills.

Long-lasting success: The importance of ethics in an organisation’s culture

Ethics in the workplace are crucial to forming a reciprocal relationship between employees and the organisation. When a company abides by high ethical standards, the stakeholders – employees, customers, and investors – will feel more relaxed knowing the organisation is protecting their interests. In return, they will work to safeguard the company’s best interests as they go about their daily work activities.

In addition, happy employees rarely quit working at companies that treat them well and encourage them to speak up against unethical behaviours. Customers also love to patronise honest and transparent brands. So an ethical company will save money on both customer and employee acquisition because there’ll be low employee turnover and/or customer churn in the first place.

In fact, there’ll be a higher level of productivity in the workplace because employees will be more devoted to their roles, knowing the company’s commitment to doing things right. More people will also want to do business with the company because of how upstanding they are.

While no organisation is fully inoculated against being accused of harassment or discrimination from a disgruntled customer or employee, having high ethical standards in the workplace can minimise potential lawsuits. When a company takes a strong stance against unethical behaviours, it’ll have little chance of being hit with frivolous lawsuits that could bankrupt the company.

In Summary

No organisation can ever be perfect, because no employee (or leader) is perfect. However, being ethical carries many benefits: it boosts trust between employees, customers, and the organisation itself; it increases engagement and productivity in the workplace; it increases customer/employee retention; and it reduces potential lawsuits.

In all, being ethical increases a company’s chances of long-lasting, sustainable success.

About the Author

Althea Storm is a B2B SaaS writer who has worked with top companies like AdEspresso, HubSpot, Omniscient Digital and Zapier to create data-driven content that drives traffic and increases conversions. When shxe’s not writing content, she’s either curled up in a chair reading a crime thriller or coding a web app.

The post Building An Ethical Organisation: Boost Morale & Consumer Trust 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.