Fair Improvement Research Articles

Dorsal Lipomodeling to Treat Donor Site After Latissimus Dorsi Flap: Safety and Effectiveness.

The latissimus dorsi flap (LDF) is a classic and efficient technique for breast reconstruction. However, its use has recently diminished in surgical practice due to dorsal disadvantages and to the increased use of microsurgical techniques for breast reconstruction, such as the deep inferior epigastric artery perforator flap. The aim of this study was to evaluate the safety and efficacy of managing dorsal problems such as asymmetry, irregularities, and dysesthesia by lipomodeling the back region during the associated surgery for breast reconstruction. A series of 300 patients operated by the last author for dorsal lipomodeling to correct sequelae after harvesting the total LDF, between November 2012 and March 2019, was analyzed. The results show a very good improvement in the dorsal region in 6.7% of cases, good improvement in 86.7% cases, and fair improvement in 6.7% of cases. There was a good improvement in dorsal comfort in 90% of cases, a very good improvement in 6.66% of cases, and a fair improvement in 6.66% of cases. In 5% of cases 2 sessions were required to obtain a satisfactory result. No major complications were registered, and the only complication encountered were oil cysts in 2.6% of cases that were treated during consultation with percutaneous puncture. This study showed that lipomodeling in the back area after LDF harvesting is an efficient and safe technique that corrects secondary dorsal sequelae such as irregularities, asymmetry, sensitivity, and dysesthesia. This technique should increase the indications for LDF because it decreases donor site sequelae, which are some of the main drawbacks of the LDF approach.

Fairness-aware fake news mitigation using counter information propagation

Given the adverse impact of fake news propagation on Social media, fake news mitigation has been one of the main research directions. However, existing approaches neglect fairness towards each community while minimizing the adverse impact of fake news propagation. This results in the exclusion of some minor and underrepresented communities from the benefits of the intervention, which can have important societal repercussions. This research proposes a fairness-aware truth-campaigning method, called FWRRS (Fairness-aware Weighted Reversible Reachable System), which focuses on blocking the influence propagation of a competing entity, in this case, with the use case of fake news mitigation. The proposed method employs weighted reversible reachable trees and maximin fairness to achieve its goals. Experimental analysis shows that FWRRS outperforms fairness-oblivious and fairness-aware methods in terms of both total outreach and fairness. The results show that in the proposed approach, such fairness does not come at a cost in efficiency, and in fact, in most cases, it works as a catalyst for achieving better effectiveness in the future. In real-world networks, we observe up to sim 10% improvement in the saved nodes and sim 57% improvement in maximin fairness as compared to the second best-performing baseline, which varies for each network.

Improved physical layer secrecy fairness in a realistic indoor NOMA VLC network under users’ mobility

This paper investigates the improvement of the secrecy rate fairness in a cellular non-orthogonal multiple access (NOMA) visible light communication (VLC) network. We formulate the users’ secrecy rate by taking into account the users (legitimate and illegitimate) mobility according to the random way-point (RWP) model. We propose an optimization problem to maximize the minimum secrecy rate for two legitimate users per cell, and then we generalize it for an arbitrary number of users. Moreover, utilizing secrecy considerations, we customize two well-known power allocation methods of fixed power allocation (FPA) and gain ratio power allocation (GRPA) as benchmarks for comparison of the proposed maximization power allocation method. Our numerical results demonstrate the superiority of the proposed power allocation algorithm compared with FPA and GRPA methods. Moreover, for the higher number of users, our results indicate the advantages of the proposed algorithm over modified FPA and GRPA methods. Finally, we investigate the effect of the optical receiver’s field of view fluctuations on the performance of the VLC network.

Comparison of efficacy of oral and topical route of tranexamic acid in patients of melasma

Background: Melasma is a common pigmentary disorder which presents with brown coloured macules that occurs around the sun-exposed areas of the skin, particularly on the face. Many treatment modalities are present but often it is resistant to treatment. The aim of our study was to compare the therapeutic efficacy and safety of tranexamic acid administered by oral and topical route in patients of melasma. Methods: Our study was a prospective, randomised study which consisted of 40 new melasma patients. 20 patients were given Oral Tranexamic acid 250 mg twice daily, classified as Group 1, other 20 patients were given topical Tranexamic acid (3%) twice a day (fully covering the lesion), classified as Group 2 for a period of 8 weeks and both were given broad spectrum sunscreen (SPF 50). MASI was determined before starting treatment (baseline), 4 weeks and 8 weeks. Based on the reduction in the mean MASI, the therapeutic response is graded and subjective response and adverse effects were also recorded at each visit. Results: Mean percentage reduction in MASI scores was higher in oral (52.1%) as compared to topical (31.9%) group. In oral group, headache was the only adverse effect, however, in topical group, erythema, burning, acneiform eruptions were reported. At final assessment, fair improvement was seen in both groups. Conclusions: Both oral as well as topical tranexamic acid were efficacious for treatment of melasma, however, of the two oral tranexamic acid was safer and provided a better proportional response.

EarWell Infant Ear Molding Device: Experience and Parent Survey.

EarWell infant ear molding device is a noninvasive, nonsurgical option to treat ear deformities in early infancy. There are no studies involving the parents' perspective to the use of the EarWell. To investigate our experience with EarWell infant ear molding device and to assess the parents' satisfaction. A retrospective case series study of infants with congenital ear deformities who were treated with EarWell device from October 2021 to November 2022. Demographic and clinical data were collected. Clinical photographs were obtained before, during, and after treatment. Parents' satisfaction level was assessed through telephone encounters and surveys, and issues associated with device application were identified. Clinical improvement of ear deformity was graded into poor, fair, good, and excellent. A total of 9 patients with congenital ear deformities were identified and included in this study (7 bilateral deformities and 2 unilateral deformities). The mean adjusted age of the initial treatment is 17 weeks (4-23 weeks), and the mean treatment duration is 5.7 weeks. The authors received full 8 responses from the parents' survey, and all parents were satisfied with post-treatment results (4 satisfied, 1 fully satisfied, and 2 very satisfied). Issues associated with device application were 3 superficial skin ulcers and 4 skin irritation. Clinical Improvement grades were: 5 excellent improvement, 2 good improvement, and 2 fair improvement. On the basis of our experience, the EarWell device has shown a high success rate and a low complication rate. It is well accepted by parents, but it needs close follow-up to detect minor device-related issues. The device is safe for multiple skin types.

A study of effectiveness of tendon transfers in upper extremity in the management of spastic hemiplegia

Over the years, most often the surgical management of cerebral palsy (CP) has focused only on lower extremities wherein surgeries are performed regularly for sitting, standing and ambulation. Upper extremity surgery is uncommonly done to improve the functions of the hand.To study effectiveness of tendon transfers in upper extremity in the management of spastic hemiplegiaThis was a prospective study, which included 24 patients with spastic hemiplegia, of either sex between the ages of 5 and 25 years. All the patients were admitted and evaluated in detail. Pre-operative functional assessment of the upper limb was done according to the House et al functional classification and recorded. IQ evaluation of all the patients was done. All the patients were subjected to the three surgical procedures.Majority (33.33%) belonged to 16–20 years and 66.67% were Females. 15 (62.5%) were having Rt. Sided involvement 20 (83.33%) were having a Normal IQ (>70). The pre-operative score significantly improved from 1.42 to 4.54 after surgery. No one was having excellent improvement but 25% had good improvement while 58.3% had fair improvement. 16.7% had poor improvement. Out of the four cases having Mild MR, majority, 3 (75%), showed Poor observed improvement scores. Out of the 20 cases having Normal IQ, majority, 13 (65%), showed Fair observed improvement scores.The appearance of the hand improved in all the 24 cases (100%) making improvement in the appearance of the hand a very important functional and psychological goal for surgical management even in severe cases

Transforaminal Endoscopic Discectomy for Thoracolumbar Disc Herniation: A Retrospective Study and Technical Note

Thoracolumbar disc herniation (TLDH) is a rare disorder with unique characteristics that can result in undesirable surgical outcomes after traditional discectomy. In view of the widespread use of transforaminal endoscopic discectomy for lower lumbar disc herniation, we investigated treatment of TLDH by this procedure. The purpose of this study was to evaluate the clinical efficacy of transforaminal endoscopic discectomy for treating TLDH and share our technical experience. We retrospectively evaluated the clinical data of 19 patients who had undergone transforaminal endoscopic discectomy for TLDH in our institution between April 2018 and July 2021. Operation time, follow-up time, blood loss, postoperative hospital stay, visual analog scale scores for low-back and leg pain, and Japanese Orthopedic Association (JOA) scores were evaluated. The differences between preoperative and postoperative JOA and visual analog scale scores were significant (P < 0.05). According to the JOA scores, 14 of the 19 patients had excellent improvement, 3 had good improvement, and 2 had fair improvement; thus, the rate of satisfactory improvement was 89.5%. Operation time, blood loss, postoperative hospital stay, and surgical outcomes were favorable. Transforaminal endoscopic discectomy is an ideal surgical procedure for treating TLDH.

Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models.

Model fairness (a.k.a., bias) has become one of the most critical problems in a wide range of AI applications. An unfair model in autonomous driving may cause a traffic accident if corner cases (e.g., extreme weather) cannot be fairly regarded; or it will incur healthcare disparities if the AI model misdiagnoses a certain group of people (e.g., brown and black skin). In recent years, there are emerging research works on addressing unfairness, and they mainly focus on a single unfair attribute, like skin tone; however, real-world data commonly have multiple attributes, among which unfairness can exist in more than one attribute, called "multi-dimensional fairness". In this paper, we first reveal a strong correlation between the different unfair attributes, i.e., optimizing fairness on one attribute will lead to the collapse of others. Then, we propose a novel Multi-Dimension Fairness framework, namely Muffin, which includes an automatic tool to unite off-the-shelf models to improve the fairness on multiple attributes simultaneously. Case studies on dermatology datasets with two unfair attributes show that the existing approach can achieve 21.05% fairness improvement on the first attribute while it makes the second attribute unfair by 1.85%. On the other hand, the proposed Muffin can unite multiple models to achieve simultaneously 26.32% and 20.37% fairness improvement on both attributes; meanwhile, it obtains 5.58% accuracy gain.

Revisiting model fairness via adversarial examples

Existing research literally evaluates model fairness over limited observed data. In practice, however, factors such as maliciously crafted examples and naturally corrupted examples often appear in real-world data collection. This severely limits the reliability of bias removal methods, inhibits fairness improvement in long-term learning systems, and probes to study accuracy-related robustness. Therefore, we ask: How adversarial examples will skew model fairness? In this paper, we investigate the vulnerability of individual fairness and group fairness to adversarial attacks. We further propose a general adversarial fairness attack framework capable of twisting model bias through a small subset of adversarial examples. We formulate this problem as an optimization problem: maximizing the model bias with the constraint of the number of adversarial examples and the perturbation scale. Our approach finds the most vulnerable examples to model fairness based on the estimated distance from examples to the decision boundary and demographic information. The experimental results 11https://github.com/TaocsZhang/Fairness-Attack-via-Adversarial-Examples. show that model fairness is easily skewed by a small number of adversarial examples.

Users' grouping algorithm for fairness improvement of NOMA‐based multi‐beams satellite networks intended for 5G

AbstractNon‐Orthogonal Multiple‐Access (NOMA)‐based Multibeam Satellite Networks (MBSNs) are intended for 5G implementation, and 5G networks set the requirement for both high system capacity as well as high system reliability to all users, which is achieved via high system's fairness. The NOMA network requires a user grouping algorithm (UGA), and most existing UGAs for 2‐users NOMA‐MBSNs, favor the system's capacity maximization with little regard to the system's fairness. Thus, this paper proposes a UGA that maximizes the fairness of 2‐users NOMA‐MBSNs. The algorithm seeks to group 2M users into M‐pairs satisfying three requirements, (i) a minimum channel‐gain margin; (ii) high channel‐correlation coefficients; and (iii) there is high fairness amongst the resulting pairs, in term of their respective channel‐correlation coefficients, to increase the users' fairness of the system. To achieve this, the 2M users are split into two sets of M‐far and M‐near users based on users' channel gains for pairing far and near users with high channel‐correlation coefficients. It will turn it into a maximization Bipartite Matching problem (BPMP) of channel‐correlation coefficients. The BPMP is addressed by means of the Hungarian Method (HM) to ensure high fairness amongst resulting pairs regarding their channel‐correlation coefficients. The proposed UGA demonstrates superior fairness compared to existing UGAs.

Fair Representation Learning for Recommendation: A Mutual Information Perspective

Recommender systems have been widely used in recent years. By exploiting historical user-item interactions, recommender systems can model personalized potential interests of users and have been widely applied to a wide range of scenarios. Despite their impressive performance, most of them may be subject to unwanted biases related to sensitive attributes (e.g., race and gender), leading to unfairness. An intuitive idea to alleviate this problem is to ensure that there is no mutual information between recommendation results and sensitive attributes. However, keeping independence conditions solely achieves fairness improvement while causing an obvious degradation of recommendation accuracy, which is not a desired result. To this end, in this paper, we re-define recommendation fairness with a novel two-fold mutual information objective. In concerned details, we define fairness as mutual information minimization between embeddings and sensitive information, and mutual information maximization between embeddings and non-sensitive information. Then, a flexible Fair Mutual Information (FairMI) framework is designed to achieve this goal. FairMI first employs a sensitive attribute encoder to capture sensitive information in the data. Then, based on results from the sensitive attribute encoder, an interest encoder is developed to generate sensitive-free embeddings, which are expected to contain rich non-sensitive information of input data. Moreover, we propose novel mutual information (upper/lower) bounds with contrastive information estimation for model optimization. Extensive experiments over two real-world datasets demonstrate the effectiveness of our proposed FairMI in reducing unfairness and improving recommendation accuracy simultaneously.

Work Related Outcomes of Revision Rotator Cuff Repair for Patients Receiving Workers' Compensation.

Workers' compensation (WC) patients are susceptible to poorer outcomes following primary rotator cuff repair (RCR). Failure of structural healing can explain some poor results and outcomes of revision RCR in this population are unknown. Retrospective review of subjects receiving WC who underwent arthroscopic revision RCR with or without dermal allograft augmentation at a single institution between January 2010 and April 2021. Preoperative MRI scans were assessed for rotator cuff tear characteristics, Sugaya classification, and Goutallier grade. Postoperative imaging was not routinely obtained unless for continued symptoms or re-injury. Primary outcome measures included return-to-work status, reoperation, American Shoulder and Elbow Surgeons (ASES) and Single Alpha Numeric Evaluation (SANE) scores. Twenty-seven shoulders (25 patients) were included. The population was 84% male, mean age of 54, 67% manual laborers, 11% sedentary workers, and 22% with a mixed profession. Average follow-up was 35.4 months. Fifteen patients (56%) returned to work at full duty status. Six (22%) returned to work with permanent restrictions. Six (22%) were unable to return to work in any capacity. Thirty percent of all patients and 35% of manual laborers changed occupation following revision RCR. Mean time to return-to-work was 6.7 months. Overall, symptomatic rotator cuff retear was found in 13 patients (48%). Reoperation rate after revision RCR was 37% (10 cases). Among patients who did not undergo reoperation, mean ASES scores improved from 37.8 to 69.4 at final follow-up (p<0.001). Mean SANE scores only improved marginally from 51.6 to 57.0 (p=0.61). No statistically significant correlation was found between preoperative MRI findings and outcome measures. Workers' compensation patients demonstrated fair improvements in outcomes scores after revision RCR. While some patients are able to return to full duty, nearly half were either unable to return or returned with permanent restrictions. This data is helpful for surgeons when counseling patients about expectations and return to work after revision RCR in this challenging population.

Intralesional methotrexate in the treatment of localized vitiligo: A pilot study.

Vitiligo is an immune-mediated skin disorder that targets epidermal melanocytes leading to the appearance of depigmented skin patches. Different treatment modalities have been reported with varied efficacy. We tried to evaluate the safety and efficacy of intralesional methotrexate in treating localized areas of vitiligo. Thirty participants with localized patches of vitiligo were recruited. They were treated with intralesional injections of methotrexate every 2 weeks for a maximum of six sessions. At the end of the study, the degree of repigmentation was categorized into: excellent improvement (>75% repigmentation), good improvement (50%-75% repigmentation), fair improvement (25%-50% repigmentation) and poor improvement (<25% repigmentation). We included 7 males (23.3%) and 23 females (76.7%). Their mean age was 33.6 ± 8.6 years. The duration of the disease ranged from 1 to 22 years. Four patients had a family history of vitiligo. At the end of the study, there was a highly statistically significant improvement (p < 0.001) after treatment regarding repigmentation. This study showed that intralesional methotrexate is a safe and effective treatment option for patients with localized vitiligo lesions. Further studies on a larger scale are needed to evaluate the long-term effects of treatment and detect the ideal dose to be injected.

Vertical accountability among ministries of state in an emerging economy: A case study of Ghana

Since 1992, Ghana has implemented several initiatives and reforms such as the National Anti-Corruption Plan (NACAP), Right to Information (RTI) Act, Economic and Organized Crime Office (EOCO), and the Office of the Special Prosecutor (OSP) with the goal of improving accountability while reducing corruption. These policies provide researchers with a lot of opportunities to examine how effective these programs have been as well as their impact on accountability. Hence, the present study seeks to accomplish three main objectives: 1) to explore the factors that facilitate vertical accountability; 2) to explore the limitations to vertical accountability; and 3) to investigate the benefits of vertical accountability in Ghana. To accomplish these objectives, we interviewed twenty directors in seven ministries of state and ten experts across civil society and academia in Ghana. We found that the disclosure of financial income, assets, and interest, the Right to Information (RTI) Act; merit-based recruitment, proper record keeping, performance-based evaluation, training and development, the absence of corruption, and conflicts of interest are major drivers of vertical accountability in Ghana. Additionally, we found that the presence of corruption, nepotism/patron-clientelism, conflict of interest, a lack of political commitment and participation, inadequate resources, and weak complaint and accountability mechanisms limit vertical accountability. Finally, the benefits of vertical accountability include improvements in openness and fairness, effective and efficient utilization of scarce resources, integrity, and trust. These results were extensively discussed, while recommendations for future studies were proffered.

Content-aware QoE optimization in MEC-assisted Mobile video streaming

The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%–12.3%, 8%–65%, 3.3%–5.7%, and 60%–130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm.

Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets

In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender’s “future dangerousness.” Such algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we use counterfactual reasoning to consider the prospects for improved fairness when members of a disadvantaged class are treated by a risk algorithm as if they are members of an advantaged class. We combine a machine learning classifier trained in a novel manner with an optimal transport adjustment for the relevant joint probability distributions, which together provide a constructive response to claims of bias-in-bias-out. A key distinction is made between fairness claims that are empirically testable and fairness claims that are not. We then use confusion tables and conformal prediction sets to evaluate achieved fairness for estimated risk. Our data are a random sample of 300,000 offenders at their arraignments for a large metropolitan area in the United States during which decisions to release or detain are made. We show that substantial improvement in fairness can be achieved consistently with a Pareto improvement for legally protected classes.

Achieving Flexible and Lightweight Multipath Congestion Control Through Online Learning

The upgrade of network devices to be equipped with multiple network interfaces makes it possible to improve network throughput performance through multipath transmission protocols, especially multipath TCP (MPTCP). However, so far the mostly used MPTCP protocols have a common limitation, namely the rigid and conservative method. They have been designed with little consideration of the fact that real networks are dynamic and the network status changes frequently, thus leading to the poor performance of current MPTCP in many realistic scenarios. In this paper, we propose a lightweight multipath congestion control algorithm based on online learning, named MP-OL. MP-OL models congestion control as a multi-armed bandit problem, and adjusts the sending rate of each subflow flexibly and adaptively through online learning. Therefore, MP-OL possesses the capability of suiting various network scenarios, and can achieve fairness and high performance in dynamic network environment. It can also flexibly switch between online learning and traditional method, which reduces the computational complexity while ensuring the learning efficiency, thus making MP-OL easy to deploy and use. As the experimental results demonstrated, compared with the leading MPTCP variants, MP-OL achieves significant improvements in fairness and link utilization, and shows better resilience to non-congestion loss and better adaptability to unstable network conditions. In real networks, MP-OL also obtains better throughput performance.

Evaluation of safety and efficacy of fractional CO2 laser in treatment of post traumatic atrophic scars

Aim: This research evaluated the safety and efficacy of fractional CO2 LASER (FCOL) in treatment of post traumatic atrophic scarring. Research design: This was a prospective, randomized research.Place and duration of the research: Outpatient Dermatology and Venereology Clinic and Plastic Surgery Departments at Tanta University Hospitals, from December 2019 to June 2021.Methodology: This research was carried out on 20 participants with post traumatic atrophic scarring treated with FCOL. Follow up 3 months after treatment and evaluation of the improvement was done by Vancouver scar scale, 3 blinded dermatologists’ assessment, and patient satisfaction score. Results: There was improvement in all participants with variable degrees, there were no scarring showed excellent improvement, 4 scarring showed good improvement (20%), 10 scarring showed fair improvement (50%) and 6 scarring showed poor improvement (30%). Adverse reactions were in general mild and well tolerated, in the form of transient redness, mild hyperpigmentation, mild pain, that all resolved within few days. No relation between the degree of improvement and age of the participants, site of the scarring, scar duration, but in sex of the participants, it was more in males than in females. Conclusion: FCOL may be an effective approach for treating atrophic scarring, from both an aesthetic and a functional perspective.

Fairness Oriented H-NOMA Devices Association and Pairing in HCN Slicing Using Cooperation Games

Device association (DA) and pairing (DP) are presented in this letter to evaluate the fairness of different services between enhanced mobile broadband devices (eMBBDs) and ultra-reliability low latency communications devices (uRLLCDs) in RAN-slicing, which has emerged as a viable technique for enhancing total network throughput in heterogeneous cellular networks (HCNs) design. To optimize the eMBBDs' downlink (DL) sum rate while fulfilling the provisions of the uRLLC traffic. A cooperative Nash bargaining solution (NBS) is formulated to depict the association method. First, base stations (BSs) create random pairings between heterogeneous non-orthogonal multi-access (H-NOMA) devices. The BSs are classified into coalitions through the Hungarian approach. So, a two-band partition algorithm is evolved for the two BSs in each coalition to negotiate their allocated devices to improve the NBS utility. A multiplayer bargaining approach is constructed using this algorithm and the Hungarian technique. After finishing the DA process, we study the cooperative matching algorithm to optimize the pairing problem between devices' slices. Simulation results demonstrate that comparing the proposed approach to other schemes can achieve a significant DL rate distribution for eMBBDs, device-side latency for uRLLCDs, and fairness improvements.

Is the Organisation for Economic Co-operation and Development’s 2021 Tax Deal Fair?

In October 2021, the Inclusive Framework of the Organization for Economic Co-operation and Development (OECD) adopted a new international tax deal, which has been hailed as a major step towards a fair and effective global corporate tax system. In this article, we question this verdict. We analyse this deal on the basis of three complementary fairness principles: preventing free riding by multinational corporations (MNCs), respect for and promotion of the fiscal autonomy of countries, and the limitation of distributive and relational inequalities. We argue that the tax deal is unlikely to lead to a substantial improvement in fairness. We do, however, welcome the deal’s recognition of the need to tax multinational corporations as unitary entities subject to a global minimum effective tax rate. We argue that this recognition creates opportunities for fairer reforms that should be pursued through a more inclusive global decision-making process. <strong>JEL codes:</strong> F230, H260.